{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64a57665",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状: (1000, 6)\n",
      "\n",
      "前5行数据:\n",
      "    月收入  年龄  性别  历史授信额度  历史违约次数  信用评分\n",
      "0  7783  29   0   32274       3    73\n",
      "1  7836  40   1    6681       4    72\n",
      "2  6398  25   0   26038       2    74\n",
      "3  6483  23   1   24584       4    65\n",
      "4  5167  23   1    6710       3    73\n",
      "\n",
      "数据描述性统计:\n",
      "                月收入           年龄           性别        历史授信额度       历史违约次数  \\\n",
      "count   1000.000000  1000.000000  1000.000000   1000.000000  1000.000000   \n",
      "mean   10182.061000    39.075000     0.507000  48783.005000     2.012000   \n",
      "std     2719.251125     9.610085     0.500201  27133.636467     1.554436   \n",
      "min     5007.000000    20.000000     0.000000   5073.000000     0.000000   \n",
      "25%     8160.250000    32.000000     0.000000  26907.750000     1.000000   \n",
      "50%    10038.000000    39.000000     1.000000  42948.000000     2.000000   \n",
      "75%    12498.250000    47.000000     1.000000  72386.500000     3.000000   \n",
      "max    14999.000000    55.000000     1.000000  99991.000000     5.000000   \n",
      "\n",
      "              信用评分  \n",
      "count  1000.000000  \n",
      "mean     79.558000  \n",
      "std       7.749754  \n",
      "min      60.000000  \n",
      "25%      74.000000  \n",
      "50%      81.000000  \n",
      "75%      86.000000  \n",
      "max      90.000000  \n",
      "\n",
      "特征变量形状: (1000, 5)\n",
      "目标变量统计: 均值=79.56, 标准差=7.75, 范围=[60.00, 90.00]\n",
      "\n",
      "训练集形状: (800, 5), 测试集形状: (200, 5)\n",
      "\n",
      "======================================================================\n",
      "各回归模型性能评估\n",
      "======================================================================\n",
      "\n",
      "=== 线性回归 ===\n",
      "R²分数: 0.6663\n",
      "均方根误差(RMSE): 4.5053\n",
      "平均绝对误差(MAE): 3.6851\n",
      "\n",
      "=== 岭回归 ===\n",
      "R²分数: 0.6663\n",
      "均方根误差(RMSE): 4.5055\n",
      "平均绝对误差(MAE): 3.6851\n",
      "\n",
      "=== Lasso回归 ===\n",
      "R²分数: 0.6284\n",
      "均方根误差(RMSE): 4.7545\n",
      "平均绝对误差(MAE): 3.8306\n",
      "\n",
      "=== 弹性网络 ===\n",
      "R²分数: 0.6376\n",
      "均方根误差(RMSE): 4.6953\n",
      "平均绝对误差(MAE): 3.7862\n",
      "\n",
      "=== 决策树 ===\n",
      "R²分数: 0.3129\n",
      "均方根误差(RMSE): 6.4649\n",
      "平均绝对误差(MAE): 5.1850\n",
      "\n",
      "=== 随机森林 ===\n",
      "R²分数: 0.6808\n",
      "均方根误差(RMSE): 4.4064\n",
      "平均绝对误差(MAE): 3.6270\n",
      "\n",
      "=== 梯度提升树 ===\n",
      "R²分数: 0.6725\n",
      "均方根误差(RMSE): 4.4632\n",
      "平均绝对误差(MAE): 3.6518\n",
      "\n",
      "=== AdaBoost ===\n",
      "R²分数: 0.7325\n",
      "均方根误差(RMSE): 4.0339\n",
      "平均绝对误差(MAE): 3.3399\n",
      "\n",
      "=== XGBoost ===\n",
      "R²分数: 0.5707\n",
      "均方根误差(RMSE): 5.1104\n",
      "平均绝对误差(MAE): 4.1135\n",
      "\n",
      "=== SVM回归 ===\n",
      "R²分数: 0.6054\n",
      "均方根误差(RMSE): 4.8992\n",
      "平均绝对误差(MAE): 3.8856\n",
      "\n",
      "=== K近邻 ===\n",
      "R²分数: 0.6497\n",
      "均方根误差(RMSE): 4.6162\n",
      "平均绝对误差(MAE): 3.6310\n",
      "\n",
      "======================================================================\n",
      "模型性能比较汇总\n",
      "======================================================================\n",
      "          模型      R²分数      RMSE       MAE\n",
      "7   AdaBoost  0.732503  4.033901  3.339942\n",
      "5       随机森林  0.680818  4.406413  3.627000\n",
      "6      梯度提升树  0.672539  4.463195  3.651774\n",
      "0       线性回归  0.666326  4.505335  3.685096\n",
      "1        岭回归  0.666301  4.505501  3.685098\n",
      "10       K近邻  0.649701  4.616211  3.631000\n",
      "3       弹性网络  0.637600  4.695263  3.786229\n",
      "2    Lasso回归  0.628399  4.754495  3.830570\n",
      "9      SVM回归  0.605433  4.899210  3.885638\n",
      "8    XGBoost  0.570676  5.110445  4.113500\n",
      "4        决策树  0.312944  6.464905  5.185000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\AppData\\Local\\Temp\\ipykernel_47852\\1301517436.py:147: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "D:\\pppython\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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UrlzZ4nx8UfYiRYpQrly5RNfHxsayY8cOGjduTOvWrVmwYEGiPsOHD2f48OGpmKmIZGVKaoiIiIiIiIhItrV582bKli1LwYIFefHFF5k8eTKbN282JzU2b97MoUOHGDt2LOPHjwegc+fOiT7o/+uvv6hYsSLdu3fn1VdfBaBt27YUKFCAHTt2WCQ1fvzxRzw9Pc3HDg4OfP/99+YPbK9cucLHH39MkyZN2LZtGwaDgb59++Lp6cmsWbN45513aNSoEV9++SUnT55k+fLlvPnmmwC8++67+Pj40KtXL/755x9sbGw4ePAgERERfPbZZ7z22msA1KlTh7lz5xIUFISHhwd16tShTp06gCmpUbly5UQf+KdU06ZN2bFjR7J9/ruqITnxq0sA8ufPj8FgoFSpUpw/f94c88MiIiIoVKhQsmPGf6hvMBi4d++euf3ff/8ld+7c5mOj0ZhsLY1XXnmFPXv2UL9+/STPu7q6Jrmt2OrVqxkwYECyMSYXe4cOHZg7dy4PHjwgV65c5jEbN25MwYIFzX337NmDr68vs2bNMrd5eHiYr0ktg8HATz/9xKRJkxg8eDALFizg119/pVWrVoApSWdra0uJEiVSNe5bb73FsGHDmDt3LpMmTcLf358DBw5YrLgBU4Jj6dKl9O/f39xma2vL//3f/xEdHc3p06eTHP+TTz7hvffeS9Reo0aNVMUpIlmD1l+JiIiIiIiISLZ09uxZLl26xHPPPUdQUBBly5bF1tbWYguqX375BcDi2+/58+c3JwfiVa5cmfXr19OkSRP27dvH1KlTadeuHQC3bt2y6NugQQN27NjB9u3bmT9/PhUrVqRDhw6sWbMGgJ9++omYmBj69u1r8YF6v379ANN2TPH/uru7mxMaYPoQ/e233+by5cucOnUKMH1wa2try5QpU1i/fj1XrlzB19eXOXPmmJM3aalw4cI0btw42Vt8kuJpLFu2LMnto+7du2fxwf6jbNiwgTfffJPbt2+b265fv46Hh4f5+OHC2I/yqIQGmJIi9+/fT3SL32rpSb355puEhYWZnwunT5/mzJkzdO7c2aJfnTp1OHXqFJMnT+bw4cNEREQwZswYBg0alOr7DAsLIzw8HHd3d6ZNm8ahQ4fo1auXxUoUf39/vL29LYqfG41GIiMjCQ0NfeS2V56envj6+pprcyxYsIDOnTuzdetWi34fffQRH374Ib/++qtFe5EiRShevPgjY4+MjEzy95BcMXIRybqU1BARERERERGRbCk+efHdd9/h6elJ8eLFiY2NZf/+/eZv79+5cwcg0Tf/ixQpYnEcFRVF3759yZs3L40aNWLp0qWPXI1QoEABGjduTJMmTejRowd79+7FxcWF9957j9jYWHMS5L/3UbRoUcC0mgBMyZL/9kmqn4+PD6tXryY2NpbXX38db29vihcvblF7ISvKmzev+fcSGxsLmLaPCggIwMvLy9zvUR+kL1q0iK1btxIZGWkuTH3w4EFKly6Nra0tBQsWxGAwmOuYREdHpzrGe/fuUbp06US3p90KqXr16pQtW9acCFuzZg3Ozs60adPGol/8yh4/Pz9q1aqFm5sbr732Gv/880+q77N///64uLiYa1E899xz5sRb/O3rr7/m0qVLFm02NjY4OTnh5uZmThL+V3zyJzw8HKPRyNKlSylbtiwtW7a06DdgwABsbGz49NNPUxX7p59+muTv4dq1a6l+HEQk81NSQ0RERERERESypS1btmBnZ8eWLVvYsWMHO3bsoFevXkRFRZm/Me7m5gZgroERLz5hEG/q1Kl88803fP7559y9e5dTp05ZbPmTnNy5c1OiRAlCQ0MJDAw0b0313w9c44/jC4p7enpy/fr1ROP9tx9Au3btOHToEPfv3+fQoUNUqVKFDz/80FxTJKuLX/lw8uRJoqKiLGpqJFWU/MKFC2zatImBAwdStmxZIiIicHNz49q1a9SqVYv8+fNz8+ZNmjVrZr7+SZIabm5uhIeHJ7rNmzfvCWeaoHPnzmzZsoV79+6xZs0aXnvttUT1O/Lly8f8+fP5999/uXbtGnPnzmX37t107Ngx1ff3/vvvs3btWjZt2sTWrVsT3d5//30AZs6cadG+ZcsW1q1bx6pVq/Dx8Uk07ieffEKNGjU4d+4cbdq04eeffyYgIIChQ4cmKuJdpkwZOnTowNatWzl27FiKY586dWqSvwdvb+9UPw4ikvkpqSEiIiIiIiIi2U5oaCi//PILdevWpXnz5uZtkeL33Y9fxVG7dm0A/u///s98bUhICBs3brQY78CBAzg5OdGvXz9zvYJvv/02RbHcunWL8+fP4+LiQr58+WjatCl2dnbMnj3bYnucr776CsD87fWWLVsSHBzMypUrzX3u37/PkiVL8PLyolKlSgBMmjSJMmXKEBsbi6OjIzVr1jTXBzl69GiiePLly0doaGiKYs8sevXqha+vL+vXrydPnjz4+voSHR1N48aNmTZtWqL+EydOxM3NjQ8//NDc9u2332JnZ0d4eDglS5bkwoULAJQrV47Zs2c/tk5HUkJCN2B03gABAABJREFUQnB2dk50i08API0333yTiIgIxo8fz/nz5xNtPRUWFoa3t7f5efPMM8+Y67EcP37cvAIlpWrWrEmbNm1o2bIlzZo1s7i5uLiwcuVK2rVrR6dOnVi7di3PPvsszZo1o3nz5rRu3ZoOHTpYJNriRUdHc+LECfr378+4ceOYPn06BQoUeGRNl/hVLpMnT05x7EOHDk3y9xAQEJCqx0BEsgYVChcRERERERGRbGf79u1ER0fTvHlzi3ZfX18KFizIli1bMBqNtG7dmsqVKzNu3DiCgoIoWbIk8+fPJywszOK6ypUrs3nzZnr06EHNmjXZuXMn69atAyA8PNyib0BAAMuXLycmJoYrV66wZMkS7t+/z4cffoi9vT3FihVjzJgxjBs3jqZNm9K6dWt+/fVXVqxYQceOHWnSpAkAH374IatXr+add97h999/x8vLi3nz5nHz5k3Wr1+Pra0tAM8//zxjx46ladOm5jofCxcuxGAw0KBBg0SPTYsWLViyZAne3t7kzp2b3377jaFDh1K+fPm0efDTwfTp0wkMDKRt27a89tpr7Nq1i/Hjx7Nx40Z69+5t0ffIkSMsX76ccePG4erqCsA///zD3Llz6dSpE23atGHixIm0atWKo0ePUrx4cXr16vVEcbm6uvLnn38mav/++++fqK7Fw5599llq1arFtGnT8PDwoFmzZhbnXVxc8PHxYdSoUfz777+UKFGCCxcusHnzZurXr59oFcSTuHPnDp9//jnTpk2jTp06LF26lFWrVvHdd9+xaNEievTowdixYylcuHCia2NjY7Gzs2PgwIEMHDgQDw8Pfv/9d3766SfGjBmDo6NjkvdZpUoV6tWrx9q1a/nrr78oXbr0Y+P8+OOP6d69e6L2pArNi0jWp5UaIiIiIiIiIpLtxK/E+O8HwQaDgWbNmnHjxg2OHz+Ovb09P//8Mx06dODbb7/lo48+wtfX11y0O96YMWPo2bMnGzduZMCAAfz777/8/PPPeHl58dNPP1l8K/7IkSO89dZbdOvWjZkzZ1KgQAG+/fZbpk+fbu4zduxYFi5cyPXr1xkwYAC//vorEydOZPny5eY+uXPnZu/evbz77rssXbqU4cOHkzdvXn7++WeLWgT169dn48aNREdHM2bMGIYMGYLRaGTVqlU0bNgw0WMzc+ZMXn31VSZOnEifPn04cuQILi4uT/eAp5GwsDCL4unxHjx4QLt27YiNjWXKlCm4urpy7do16tSpw99//23uFx4ezjvvvIOjoyN9+/YFTCsFevTogZ2dHZ9++ilFixZl+fLlnDt3jv79+1vcz507d5K8fzB9SB8bG8vFixc5deoUQUFBjywUHl+A/MyZMxw7doz79+8/0eMRXyS+ffv2ODg4JDq/YsUK3n33XVasWEGfPn1YsmQJXbt2ZfXq1U90f2CqE7J27Vo6dOiAl5cXU6dOZeDAgezYsQMXFxfeffdd/P396devH4sWLaJUqVKMHj060Rzjt/Py8PAwF2efP38+9vb2FomoBw8eJIqhX79+if52IaG2yoMHD/j99985e/Ys8OhC4XFxcQQFBXH69GnOnz//xI+JiGQuBuPD6xxFREREREREREQy2KJFi1i9ejXHjh3Dw8ODc+fOmc8dO3aMt956iytXrrB+/XpeeuklAG7evEnTpk25efMmP/30E76+vjx48IAPPvgAW1tb8/Zg77zzDkuWLGHatGkWqyfefPNNzp8/z969e7l16xYvvPAC165do1KlSpw4cSJRjD4+PpQrVw53d3dWrVqFnZ3dY1dDxMbGEh0dza+//kr16tXT4JFKP3fu3KFNmzbs378fo9GIu7s7nTt3ZuDAgZQqVSrJa86dO0fPnj3Zt28fNWvW5ODBg+akUJUqVcibNy979uwx9zcajRw/fpxq1apx9OhRWrRoQUhICFFRUezatSvJJNzD1qxZQ4cOHdi4cSPt2rXD0dHRvGLpUeLi4oiLi6NRo0Zs2LAhdQ+KiGRKSmqIiIiIiIiIiIhV7du3jxYtWlChQgUmTpxo3oLLz8+PQYMGUb58eVasWEGVKlUsrgsKCuKFF17Aw8OD/fv3m9vDw8NxdnYG4NChQ6xdu5bPPvvM4to7d+7g4uKCk5MTAG+//TbPPPMMPXv2pHjx4olifPbZZylfvjybNm1Ky6lnKvHboL3yyis0btwYe3v7x15jNBpZtGgRtWrVokKFCuZ2Hx8fChQoYJHU+K8yZcpQqFAhXnzxRcaOHYudXfI75S9btoyuXbty5MgRnnvuuRTPS0SyFyU1REREREREREQkU4qNjWX16tXmb+Un5fr169jZ2SVZpFpERLIfJTVERERERERERERERCRLUKFwEZEsbteuXfz6669PfP2AAQOYMWNGovb9+/dTtWpVbt++DcDIkSMZMmTIE9/Po/Tp0ydTLN+Oi4vj7t275oJ+/7V3714+/vhjAgMDUzzmtWvXaNWqFT/99FOS5+/fv0/r1q0ZO3bsE8UsIiIiIiIiIpLTJL9RnYiIZHqjR4/m/PnznD9/nvz586f6+s2bN1OtWjUAYmJiuHXrFk5OTgQHB3Py5EnCwsKwtbXl9OnTREZGcvfuXSIjI7G3t8fDw8M8zpQpU4iIiEhyD1Sj0UhkZCTDhw/Hzs6OqKgo3NzcAFi4cCFFihThlVdeecJHIG3cvHmTIkWK8NFHH/Hpp58mOv/rr78yfvx43nzzTTw9PVM05pEjR8wF7JKSO3du8uXLx4QJE6hduzYtWrR4qjmIiIiIiGSkmzdv0qFDB3Nh6NSoXLkyX375ZTpEJSIi2Z2SGiIiWdjp06c5cOAAgwYNIigoiKCgoCT7FShQwCIB8TBnZ2dzYbyrV69SokQJi/Pe3t4Wx3nz5gWgb9++fPXVV+b23bt3c/v2bS5dugRA+fLlATh48CCFCxfG09OTAQMG8Omnn7JgwQLzigdHR0fz/VtTfBHB+H//y8HBAeCR+/g+7NChQzg6OrJq1SocHByoUqUK58+fJzIyktKlS7Nx40YcHR1xdHSkefPm3Lx5k+joaH766SciIyOJiIigRo0alCxZMu0mKCIiIiKSxiIiIujUqRO9evVK9bU9evRIh4iyljt37hAUFMQzzzxD7ty5H9nv6tWrhIeHU6JEiccW0hYRyQm0/ZSISBY2YMAAAKZPn0758uWTvNWuXZt79+49cgwbGxvzN6u8vb158OABMTEx7N69G4B79+5hNBpp164drVq1wmg0Eh4eztSpUy3G2bZtG0ePHqV+/fo0atSIX375hV9++YXcuXPTvXt3jh07Rv78+XF2drZIHNjb22Nra2s+Ll68OAaDgYEDB5rbgoODMRgMGAwGFi9enGgOzz77LPny5SM2NjbRufjrkro9vO1VfAyPepMQn9SI/zdeaGgoP/zwg0XbW2+9Rf369Vm9ejUGg4H69etTrVo1fH19CQgIYPTo0YwZM4aRI0cyceJErl27xrhx4xgxYgRjxoxh/PjxHD9+PMk4RERERETSk7+/v8VrZltbW7y8vGjfvj0nT560dnhpLjw8nGHDhuHl5UWuXLl44403HvllseQsXrw42fceL774orlvUFAQr776Kp6enpQtW5Z8+fLRu3dvIiMjLcY8fvw4lSpVwsvLizJlylCgQAFmz579tFMWEcnylN4VEcmiFi5cyM6dO3nppZc4d+4cp0+fNq+iAFi9ejWdOnVi7ty5FCtWzOLaS5cusWrVKuzt7QkMDOTPP//ks88+o0CBArz99tuEhoZStGhRFi1aRGRkJDExMXTq1Amj0cjdu3exs7N75DeJoqKiyJMnzyPjtrW1xcbm8Tn1hz/UP3HixCP7nTt3jn/++QcwrQqpV69eoj41atSgf//+idp9fX3NPz8upqTOnz17lg4dOvDnn3+yadMmmjdvDsCFCxc4duwY1atXZ/Xq1bRq1criur/++gswJWse/p3dvXsXV1fXFD0+IiIiIiLpqUmTJnTt2pWIiAjOnTvHvHnz2Lx5MwcOHKBq1arWDi/NtG/fni1btvDWW29RrVo1vvrqK1577TV++eWXVL0uf/7551m2bFmi9oiICPr06UOZMmUA09a8b7zxBv/88w9r166lePHiLFmyhBkzZlC4cGFzvb3AwECaNm1KxYoV2bdvH3Z2dowfP54+ffrg4+PDCy+8kDYPgIhIFqSkhohIFnTkyBH69u1L7969mTZtGlWqVKF79+6sXbsWMBW1fvvtt+nfvz8dOnRIdP3du3fZtm0buXLlIjAwEIPBwI4dOyhfvjxNmjTBy8sr2fuvVasWBw8eNB8HBgZiY2ODo6MjERERGAwGi4Lb0dHRBAcHp+pNwcPfAksuqbF582aLn5NKanh7e9OlS5cU3/fjRERE8PnnnzNlyhTi4uIYOnQo9evXt+izfft2cuXKxcsvv5zkGL/99hvt2rVj06ZNVK9encDAQBo1akSTJk2YNm1amsUqIiIiIvIkypQpY/Ea+pVXXqFhw4bMmTOHOXPmWCWm+/fvW7zPSEpq6gx+//33bNmyhcGDB/PFF18A0LJlS8qXL8/y5cvp2rVriscqWbJkktvHTpkyBXt7e8aNGwfAli1b2L17N4cOHaJmzZqAaeX9nj17WL9+vTmp8fnnn2NjY8OGDRvMXxpbtWoV+fLlY8OGDUpqiEiOpqSGiEgWc/nyZZo2bUrVqlWZPn06Tk5OrFy5kvr16zNkyBAaNGhAp06d6Nq1KzNmzEhyDF9fX/bs2cOVK1fw9vamcePG5m2d4pdaHzhwgNq1aye6dsqUKWzZssWirU6dOly8eNGibfny5eafJ02axKRJk3j77bcpVapUiuZ59+5dLl26RIkSJZLdimnz5s1UqFABNzc3Nm/ezKRJk1I0/qNs376d+/fvYzQaiYqKIioqitatW5vPz507l/nz53Pjxg06d+7MhAkTKF68eKJxfvjhB1q1asWRI0fo3Lkz3333nfmNx/379+nWrRtBQUHm2iIxMTFEREQwffp0fHx86N69+1PNQ0REREQkLb3wwgs4OTnh7+9vtRg++OADlixZkmwfo9GY4vHWrFmDjY0Nw4cPN7eVLl2axo0b83//93+pSmok5c6dO0yZMoVBgwZRpEgRACpWrMiGDRvMCY14jo6OxMTEmI87depE69atLVbBx9f3e3j7XhGRnEhJDRGRLKZYsWLMmTOHhg0bmgtsV69enXnz5tG1a1emTZuWqIj3o/zf//0fYEpkzJgxg4EDB5prStSpU+eR1/33W0HffPMN0dHRxMTE0K5dO3r16kX79u0B0ze62rVrR4cOHShUqBBr165NtFfsfzVs2JDdu3dz4sQJSpQowYkTJ3jppZfYtWuXRb/Q0FB++eUX3nvvPdzd3Zk0aRJXr16laNGij537oxw6dIg//vgDo9FIZGQkUVFRlCxZ0vzGYeLEiXTo0IFRo0aZi6H/17Fjxzh69Cjjxo3DxsaGa9euWZzv3r07Fy5cYO7cuTRr1gyAwoUL8/PPP1O3bl369OlD2bJlE63+EBERERGxlqCgICIiIihQoIDVYhg2bFiarsA+deoUFStWxNPT06K9evXqj02epMTnn3+OwWBg2LBh5rZixYol2h54z549HD58mDFjxpjbqlWrlmi8L774gri4OFq0aPHUsYmIZGVKaoiIZEFvvPGG+efr168zf/58Zs6cSalSpXBycmLBggXmfvXq1UvymzxGo5H58+cDpjcoQ4cO5bnnnqNChQoA7Nixg+eeey7RdX5+fuzZs8eiLX6LpRkzZhAbG0v37t3N9SpsbGwoVqyY+YX3ihUrLJaMG41Gc6HyeN7e3hQoUIDjx4/TokULzp49S/PmzRMlNXbs2EF0dDTPP/88efPmZdKkSWzZsoX333/fol9UVFSiYn92dna4u7snmt+YMWMYMWJEovb4JNGRI0eS3UPYaDQyePBgcufOTfHixTl9+jQAV69e5dy5cwQFBbFx40aGDRtmsRrjwYMH5M2bl3Xr1vH2228nKkguIiIiIpKRIiIiCAoKwmg04u/vb36N3LlzZ6vF5OPjg4+PT5qNFxwcTN26dRO1FyxYkOvXrxMVFfXEr8vv3bvHnDlz6NWr1yNrDm7evJmlS5fyww8/0Lp1az766KMk+82cOZO1a9fyyy+/8Nlnn9GwYcMniklEJLtQJVIRkSwoICCAefPm0bRpU7y8vNi4cSN+fn6cO3eOkydPsnDhQk6ePMkLL7xAvnz5eOmllxg4cCDTp0/n6tWrAKxcuZK///6bggULUq5cOd566y1GjhxJbGzsY+//v32MRiOLFi1i5MiRNG7c2KIAd1RUlEUS44svviA4OBh/f38OHDhASEiIecXJw2rUqMGJEyc4ffo0MTEx1KhRI1Gf+Hoazz//PPXq1cPe3t6ixka8H3/8EU9PT4tbUltrpUShQoWSPW80Grlw4QJhYWHUqlXLnGDp27cv9evXJ0+ePBw6dAh3d3dKlChBXFwcAJs2bSJ//vwcO3aMU6dOJVqOLiIiIiKSkRYsWICnpycFChSgZs2anDhxgm+++ca80jg7iIuLI3fu3InaXVxcMBqNhIaGPvHY8+bNIywsjP79+z+yz5UrVzhx4gSxsbHY2dkRFRWVZL+zZ89y7ty5ZPuIiOQkSmqIiGQRsbGxvPfeezz77LMUK1aMTz/9lLJly3LgwAEOHTpE586dMRqNxMXF8frrr7N7927OnTvHmDFjcHJyYvHixcyZM4eCBQsSGBjIiBEj6Nu3r/lD+hEjRhAYGMiVK1cAaNKkCXnz5k10Gz9+fKLto4YPH867776Lp6enefVHvLfeegsfHx+io6Mt2mfMmEHdunUxGAxJbrNUo0YNjh8/zvHjx3FwcKBy5coW541GI1u3bqV48eLY29sTHh5OpUqV2LlzZ6L4GjRowI4dOyxuixYterJfxGPY2Nhw9OhRoqKiePDgAdu3bwdMSYugoCCqVKlC5cqVKVq0KFeuXOHw4cMA/PLLL0RFRfHCCy9oj1wRERERsbpWrVqxY8cOZs6cCUCfPn3o3bu3VWMKCQnh5s2byd5Sw8XFJcn2+LocjytK/ihGo5Gvv/6a1q1b88wzzzyyX69evTh//jz/93//x7p16x75+M6ZM4crV64waNAgxowZY16ZLyKSU2n7KRGRLMLW1pbmzZtTtmxZmjRpwu3bt2nUqBGzZs167LX+/v54e3tz584d7O3t+fTTT4mNjeWTTz6hQYMGAJQtW5Y//vjDXBsj/oP1zp07ExwczNatW83jxcXFERkZaS5UN378eG7evMmECRMS7Q87f/58PD09+fXXXy0SHh988AFFixalVatWlClTJlHMNWrU4OOPP2bnzp1Urlw50bLv33//3fym5b974O7Zs4emTZuajwsUKEDjxo0f+zillcKFCz/yXGxsLLa2trRr146+ffuyatUqatasyYYNG2jUqBHlypXLsDhFRERERB6laNGiNG7cmMaNG7N48WK+/vprhg0b9sitlDLChx9+mKaFwosUKUJAQECi9sDAQABcXV1TF+D/7N+/n0uXLjF9+vQU9W/Xrh1vvPEGa9asYfHixUl+ycnBwYHJkyezfPlyVq5cabGVrYhITqOkhohIFtK2bVvzz7/88gsAy5cvp1atWkn2X7t2LcOHD8fJyQmDwUC+fPkAmD59Ou+//36iNyRXrlyhdOnSSY7137oXxYoVw9/fHwBnZ2eWLl3K8uXL2bZtGw4ODuaC42BKggQEBLB8+XKLNg8PD27fvp3k/cVvN7Vq1Sp69eqV6PyWLVsA03ZWVapUAeDvv/+md+/ebNmyxSKp8aSioqL49ttvEyVNUiIsLIzLly9z6NAhAD799FPu3LnD6dOnuXv3Li4uLrz77rssXLiQMmXKcOXKFWbPnv3UMYuIiIiIpLVRo0bRvn17Zs+ebVH0OqOldaFwX19fli5dSlhYmMWqjcOHD5MrV64nTmosW7YMd3d3mjdvnujcxo0buXjxIgMHDrRoz58/v8Xq9hkzZlCyZElatWpl0S9fvnyJVsGLiOQ0SmqIiGRR9vb2gOnbRaVKlUqyT4ECBQAsEgxg2iIpqQJ7xYsXJyAgAGdnZ+zs7Ni9ezdt27bF2dkZo9HInj17KF26NPfv3+fGjRsW1xqNRvr06UN0dDSOjo4W93n37l327dvH77//bm6Ljo4mIiKCQYMGUadOnUSxeHp6UqxYMS5fvvzIehpOTk707t3b/AakYcOGfPTRR2zevNm8TP5JREdHs2zZMiZMmIC/vz8jR45Mdtn41KlTeeGFF6hZsyaXL1+mZs2a3Lp1C8D8LStXV1dee+01ypUrZ/7dDRkyhNmzZ/PBBx9QrVo1czF1EREREZHMpG3btvj4+DBjxgz69++fZE28jJDWhcLbtm3LN998w7fffmuufXHt2jV++umnJ64dEhcXx7p162jatKl5ZfvD/vnnH4YMGULNmjWpV68eYPoy1ebNm6lTp475/cPevXuZNm0a9erVI3/+/AD89ddf/Pnnn1ZNLImIZAZKaoiIZFHxH4ynREprNNjZ2eHl5QXAvXv3GD9+PIUKFeLnn3+mVq1a9OjRg19++QVvb2+8vb0trjUYDI8spFe8eHFat26Nn59fimMG02qNy5cv89xzz1m037p1i6NHj9KkSROLb1TZ2trSpEkT1qxZw/nz5ylbtixAolUi8YoVK2befuvff/8FTMmSb775hitXrlC/fn2+/vprWrRowVdffQUkXs4eEhLCRx99RMmSJTl//jzFihWjdevWeHt789JLLxEZGUnDhg354IMPzPcVLzIykvz583P9+nUaNGiQaDWMiIiIiEhmYDAY+Oijj+jSpQuLFi2yem2NtNKoUSOef/55hg4disFg4Nlnn2X48OFERUVZFPj+559/+O2332jYsCFFihRJdsyjR49y+/ZtXnjhhSTPd+/enfnz59OsWTP69u2Lt7c3K1asICAggIULF5r7ffLJJ9StW5caNWrQp08fbGxsmDlzJkWLFk22+LiISE6gpIaISBYXEBDAuXPnkjz339UUKXXx4kW6dOnCuXPnWL9+PRUqVODbb7+lU6dONGrUiB9//DHZuhFppUaNGmzevJkKFSqYC5gDbN26lbi4uCS/PdW8eXPWrFnD5s2bzUmNI0eO8NZbbyXq265dO3OiYefOnYBpW69GjRqxfPlynn/+eXPf+KXn06dPN3+jCkxbfMXExNC1a1dz29y5c80/x28TFhcXZ267efMmX3/9NdOmTcPDw4MOHTowc+ZMdu/eTc+ePXn11VfNySURERERkcygY8eOfPzxx0ydOpX33nsv0WrwrOr777+na9eu5kSBs7Mzs2bN4qWXXjL32bdvH926dePHH398bFJj27ZtABbvJR6WJ08edu7cyejRo1m6dCnh4eHUrFmT/fv3U7NmTXO/ypUrs2/fPj766CMmTZqEs7MzzZo1Y8KECRQsWPBppy0ikqUZjKmpoCQiIpnG0aNHk9yWKSlBQUHmehr/ValSJfNesnv27GHVqlUsXrwYDw8Pli1bRqNGjcx958yZQ79+/cidOzfDhw+nZ8+e5M2blx9++IHQ0NBHrh4ZOHAg1atXT3L/29jYWCIiIvD19bV4EZ/RoqOjad++PX369EmyHkdwcDBNmjSx2EIrXpMmTVi3bh25cuVKdG7Xrl00atSIn3/+GS8vL3r27Mmvv/5KTEyMOZlRoEAB1q9fz4gRIzh//jxg+gbXt99+m/YTFRERERFJI/7+/rz88svJbtX6KM7OzmzdujUdonoyx48f58qVK9SsWZNChQpZOxwREUlG9kiri4jkQOHh4QDs3r2bF198Mck+ixcvplu3bkRERDxynLCwMPNYBw8eZOXKlfTr14/Ro0fj5uZm0bdXr16UL1+eAQMGsHHjRvr16weYitidOnUKOzu7R26hdPToUY4ePZqoPTY2lvDwcEaOHGnVpIa9vT0bNmx45Pm8efNy9OhRIiMjLQrz2dnZJbuncGRkJGDaJ7dMmTKUKlWKwoULM2zYMKpWrWru17p1a1577TV++uknVq5cabHyQ0REREQkMypevDgXLlywdhhpwtfXF19fX2uHISIiKaCVGiIiWVR4eDjXrl2jSJEiODs7p8mYsbGxPHjwwLzV0qMYjUbu3bv32H4iIiIiIiIiIiJpSUkNERERERERERERERHJEmysHYCIiIiIiIiIiIiIiEhKKKkhIiIiIiIiIiIiIiJZQo4rFB4XF8f169fJkyfPI4vZioiIiIhI6sTXW3rmmWewscn+353S+woRERERkbSV0vcUOS6pcf36dby8vKwdhoiIiIhItnTlyhWKFi1q7TDSnd5XiIiIiIikj8e9p8hxSY08efIApgfG1dXVytGIiIiIiGQPoaGheHl5mV9vZ3d6XyEiIiIikrZS+p4ixyU14peGu7q66s2HiIiIiEgayylbMel9hYiIiIhI+njce4rsv9mtiIiIiIiIiIiIiIhkC0pqiIiIiIiIiIiIiIhIlqCkhoiIiIiIiIiIiIiIZAlKaoiIiIiIiIiIiIiISJagpIaIiIiIiIiIiIiIiGQJSmqIiIiIiIiIiIiIiEiWoKSGiIiIiIiIiIiIiIhkCUpqiIiIiIiIiIiIiIhIlqCkhoiIiIiIiIiIiIiIZAlKaoiIiIiIPEa/fv0wGAzmW6lSpVJ03d9//42Hh0ei9qlTp1KwYEFcXV1p164dt2/fNp979dVXLe6rcePGaTYPERERERGRrE5JDRERERGRx/j999/ZvHkzwcHBBAcHc/z48cdec+nSJVq2bElwcLBF+759+1iyZAn79u3j2LFjREREMHjwYIv7OnXqlPm+NmzYkObzERERERERyaqU1BARERERSUZMTAynT5/m+eefx93dHXd3d/LkyfPY61q2bEmPHj0StR8+fJgWLVpQtmxZSpUqRadOnbhw4QIAV69exWg0UrFiRfN95cqVK83nJCIiIiIiklUpqSEiIiIikow//vgDo9FI1apVcXZ2plmzZgQEBDz2uk2bNvH6668naq9YsSJr167l4sWL3Lp1iwULFtCkSRPAlPCIjY2laNGi5MqVi44dOyZa6SEiIiIiIpKTKakhIiIiIpKMs2fPUqFCBVauXMmZM2ewt7enZ8+ej72uZMmSSbY3a9aM0qVLU6pUKQoWLMiDBw8YMWIEABcuXKB69eps27aNo0eP4u/vz0cffZSm8xEREREREcnKDEaj0WjtIDJSaGgobm5uhISE4Orqau1wRERERCSLuXz5MiVLliQ4OPixryf9/f0pUaIED7/kXrNmDR9//DHr1q3D09OTIUOGEBISwg8//JDo+r1799K+fXsCAwPTfB5pLae9zs5p8xURERERSW8pfY2tlRoiIiIiIqng7u5OXFwcN27ceKLrV65cSe/evSlbtiweHh74+fmxdu1a7t69m+R9BQUFERkZ+ZRR51yLFi0y1yjp1KkTQUFBAJw+fZoaNWqQN29ehg4dSg77rpeIiIiISJalpIaIiIiISDIGDRrEmjVrzMdHjhzBxsYGLy+vJxovJiaGf//913wcnxyJjY2lffv2HDx40OK+ChUqhKOj4xNGn7P9/PPP9O/fnxkzZnDy5ElCQ0Np06YNkZGRvPrqq1SvXp2jR49y5swZFi9ebO1wRUREREQkBeysHYCIiIiISGZWtWpVRo0aRaFChYiJiaFfv3688847uLi4EBoairOzM/b29iker169ekyfPp2iRYvi7OyMn58fderUIV++fFSuXJmBAwfi5+dHYGAgY8aMoU+fPuk4u+xt6dKl9OjRw1yIferUqVSoUIFNmzYREhLC9OnTcXFxYdKkSfTt25du3bpZOWIREREREXkcJTVERERERJLRtWtXzp49S6tWrciTJw9t2rRh0qRJAFSuXBk/Pz9at26d4vEGDBjA9evXmTBhAkFBQdSpU4cFCxYAMHLkSC5fvkyTJk0oUKAAvXv3ZuTIkekxrRwhKCgIX19f87GtrS1g2nqqdu3auLi4AKbf45kzZ5IdKzIy0mIbsNDQ0HSIWEREREQkg+3fD7a2ULeutSNJMRUKFxERERGRp5YZX2d/9NFHHDhwgF27dmEwGBgxYgS7d++mfv36RERE8PXXX5v7enp6cuHCBfLmzZvkWB9//DHjx49P1J6Z5isiIiIikmL37sHIkfD11/Dss/DHH/C/L/1YS0rfU2ilhoiIiIjkaCtDV1o7hKfWybWTtUPIlIYMGWKuneHk5MSBAwdYunQpp0+fTlSnxMnJibCwsEcmNUaOHMmgQYPMx6GhoU9cV0VERERExKq2b4f33oOAANPxxYswbx4MGGDVsFJKhcJFRERERCRb8vDw4Ndff2XNmjVUrlyZcuXK0blzZzw8PAgMDLToe+/ePRwcHB45lqOjI66urhY3EREREZEsJTgYunWDpk0TEhouLjBjBvTrZ93YUkFJDRERERERydaeeeYZ1q5dy+TJk7G1taVGjRocPHjQfN7f35/IyEg8PDysGKWIiIiISDpatw58fGDx4oS2l16CU6dMKzT+V38uK1BSQ0REREREsrVZs2ZRrlw5c0H3559/npCQEJYuXQrAlClTaNy4sbmQuIiIiIhItnHrFrzxBrRtCzdvmtpcXWH+fPj5ZyhZ0rrxPQHV1BARERERkWzr7t27fP755/z000/mNjs7O+bNm0fnzp0ZOnQosbGx7N2714pRioiIiIikk+Bg2Lgx4fiVV2D2bCha1HoxPSUlNUREREREJNtyd3fn9u3bidpbt27NX3/9xdGjR6lbty6enp5WiE5EREREJJ2VLQvjx8PUqTBrFnTsCAaDtaN6KkpqiIiIiIhIjlSkSBGKFCli7TBERERERNJGXBx89x20bw/OzgntgwfDu+9CNvkij2pqiIiIiIiIiIiIiIhkZRcvQqNG0LUrfPyx5Tk7u2yT0AAlNUREREREREREREREsqbYWJg+HSpVgj17TG3TpsHly1YNKz1p+ykRERERERERERERkazmzz+he3c4dCihrVgxmD/f9G82pZUaIiIiIiIiIiIiIiJZRXQ0TJgAvr4JCQ2DAfr1g9OnoUkT68aXzrRSQ0REREREREREREQkK/j9d1PR7z/+SGgrWxYWLIB69awXVwbKsis1+vXrh8FgMN9KlSpl7ZBERERERERERERERNLPtm0JCQ1bWxgxAk6cyDEJDcjCKzV+//13Nm/eTN26dQGwtbW1ckQiIiIiIiIiIiIiIulo6FD4/nuIi4OFC6F6dWtHlOGyZFIjJiaG06dP8/zzz5M7d25rhyMiIiIiIiIiIiIikrbu34c9e+CVVxLa7O3hxx+hQAFwcLBaaNaUJbef+uOPPzAajVStWhVnZ2eaNWtGQEBAkn0jIyMJDQ21uImIiIiIiIiIiIiIZFo7dkDFitC6NRw7ZnmuaNEcm9CALJrUOHv2LBUqVGDlypWcOXMGe3t7evbsmWTfyZMn4+bmZr55eXllcLQiIiIiIiIiIiIiIikQHGwqBP7yy3D5MsTGQr9+YDRaO7JMw2A0Zv1H4/Lly5QsWZLg4GBcXV0tzkVGRhIZGWk+Dg0NxcvLi5CQkER9RURERCTnWRm60tohPLVOrp2sHQKhoaG4ubnlmNfZOW2+IiIiIpIB1q+H3r3h5s2EtoYNYf58ePZZq4WVUVL6GjtL1tT4L3d3d+Li4rhx40aiyTo6OuLo6GilyEREREREREREREREknHrlmk1xpo1CW158sAXX8B774HBYL3YMqEsuf3UoEGDWPPQL/jIkSPY2NhoaykRERERERERERERyRqMRvjuO/DxsUxotGwJZ87A++8roZGELLlSo2rVqowaNYpChQoRExNDv379eOedd3BxcbF2aCIiIiIiIiIiIiIijxcbC59/Drdvm47z5YOZM6FzZyUzkpElkxpdu3bl7NmztGrVijx58tCmTRsmTZpk7bBERERERERERERERFLGzg4WLoRataBdO5g1CwoUsHZUmV6WTGoATJ48mcmTJ1s7DBERERERERERERGRx/vnH4iMhPLlE9qqV4dTpyzbJFlZsqaGiIiIiIiIiIiIiEiWEBsLM2ZAxYrQpQvExFieV0IjVZTUEBERERERERERERFJD2fOQP36MGgQhIfDsWPwzTfWjipLU1JDRERERERERERERCQtRUfDxIng6wsHDya0f/ABdOtmvbiygSxbU0NEREREREREREREJNM5fhzefRdOnEhoK1MGFiwwrdqQp6KVGiIiIiIiIiIiIiIiTysiAj76CGrUSEho2NrC8OGmYyU00oRWaoiIiIiIiIiIiIiIPK0//4TPPoO4ONNx5cqwcCFUr27duLIZrdQQEREREREREREREXla1avDwIFgbw+ffAJHjiihkQ6U1BARERERERERERERSa1ffoGYGMu2Tz4xbTU1Zgw4OFglrOxOSQ0RERERERERERERkZS6exe6d4cGDWDGDMtzLi7g42OVsHIKJTVERERERERERERERFJiwwZT0mLhQtPx2LHg72/VkHIaJTVERERERERERERERJITGAgdO0Lr1nDjhqktTx7w8wNvb2tGluPYWTsAEREREREREREREZFMyWiElSuhf3+4fTuhvUULmDMHvLysF1sOpaSGiIiIiIiIiIiIiMh/XbsGvXrBpk0JbR4eptUZXbqAwWC10HIyJTVERERERERERERERP7ryy8tExrt28NXX0HBgtaLSVRTQ0REREREREREREQkkbFjoWRJUxLjhx/g+++V0MgEtFJDRERERERERERERHK22Fj44w/w9U1oy5UL1q+HIkVM205JpqCVGiIiIiIiIiIiIiKSc509Cw0aQL168PfflucqVVJCI5NRUkNEREREREREREREcp7oaPj0U6haFQ4cgPBweP99MBqtHZkkQ9tPiYiIiIiIiIiIiEjOcvw4vPsunDiR0Fa6NIwfDwaD1cKSx9NKDRERERERERERERHJGSIiYNQoqFEjIaFhYwPDhsHJk6ZtqCRT00oNEREREREREREREcn+fvsNuneHc+cS2ipVgoUL4bnnrBeXpIpWaoiIiIiIiIiIiIhI9hYTA127JiQ07O1NW00dPaqERhajpIaIiIiIiIiIiIiIZG92djBnjunnGjXg2DEYOxYcHKwbl6Satp8SERERERERERERkezl7l0IC4Nnnkloa9wYtm41/Wunj8azKq3UEBEREREREREREZHs48cfoUIF03ZTRqPluWbNlNDI4pTUEBEREREREREREZGsLzAQOneG116D69dh505TEXDJVpSSEhEREREREREREZGsy2iE1auhXz8ICkpob9YMmjSxXlySLrRSQ0RERERERERERESypuvXoXVr6NQpIaGRNy8sWQJbtoC3t1XDk7SnpIaIiIiIiIiIiIiIZC1GIyxYAD4+sHFjQnu7dnDmjKmehsFgvfgk3Wj7KRERERERERERERHJWg4fhh49Eo4LFoSvvzYlNSRb00oNEREREREREREREclaatWCd94x/dy1q2l1hhIaOYKSGiIiIiIiIiIiIiKSufn7m7acetj06fDTT6b6GR4eVglLMp6SGiIiIiIiIiIiIiKSOUVHw6RJULYsLF5seS5vXmja1CphifUoqSEiIiIiIiIiIiIimc+JE6ZtpkaNgqgoGDQIrl+3dlRiZUpqiIiIiIiIiIiIiEjmERkJo0dDjRpw/LipzcbGVBjc3d2qoYn12Vk7ABERERERERERERERAA4cgO7d4ezZhLaKFWHhQlOSQ3I8rdQQEREREREREREREet68AAGDoR69RISGvb2MG4c/P67EhpippUaIiIiIiIiIiIiImJd48aBn1/CcY0asGABVKpktZAkc9JKDRERERERERERERGxrpEjoUABcHKCqVPht9+U0JAkaaWGiIiIiIiIiIiIiGSsGzegcOGE43z5YNUqKFoUSpe2XlyS6WmlhoiIiIiIiIiIiIhkjKAgePNNqFABbt60PNewoRIa8lhKaoiIiIiIiIiIiIhI+jIaYfVq8PGBFSsgOBg++MDaUUkWpO2nRERERERERERERCT9XL8OffrAhg0JbXnzwquvmpIdBoP1YpMsRys1RERERERERERERCTtGY2wcKFpdcbDCY22beHMGXj7bSU0JNW0UkNERERERERERERE0pa/P7z/PuzYkdBWoAB8/TW0b2+1sCTrU1JDRERERERERERERNJOTAy89BJcupTQ9tZbMGMG5MtnvbgkW9D2UyIiIiIikm0tW7YMb29vcufOTePGjfH39wegX79+GAwG861UqVLWDVRERCQHiY2K5diCY+wet5tjC44RGxWbZmMb44zcvnCba0eucfvCbYxxxjQbOz2lZ9xWeUzs7GDyZNPPRYvC5s2wdGmKExpxMXH8ve1vTiw5wd/b/iYuJi7NQkvP5196xi0JtFJDRERERESypYsXLzJq1CjWr19P/vz5GT9+PO+88w579uzh999/Z/PmzdStWxcAW1tbK0crIiKSM+yfvJ8D0w8QGRKJMc6IwcbAzyN+ps6gOjQY2eCpxr5x/AYnl5wk8GwgMREx2DnZ4VnekypvV6Gwb+E0mkHaS8+4M+wxiYmBsDBwdU1oe+MNCA6Gzp0t2x/j1MpT/Pr5r4ReCSUuOg4bextcvVypN6welTpVeqow0/P5l55xiyUlNUREREREJFs6fvw4tWvXplq1agB069aNN954g5iYGE6fPs3zzz9P7ty5rRyliIhIzrF/8n72jt9LXHQctk622NjaEBcbR8SdCPaO3wvwxB8s3zh+g72f7CUsKAzXoq7Y57In+kE013+/zt3Ld3lh7AuZMrGRnnFn2GNy8iS8+y6UKgWrVye0GwzQq1eqhjq18hTbB28n6n4UTnmdsHO0IyYyhuCLwWwfvB3giRME6fn8S8+4JTFtPyUiIiIiItmSj48Pu3bt4vjx44SEhPD111/TpEkT/vjjD4xGI1WrVsXZ2ZlmzZoREBCQ7FiRkZGEhoZa3ERERCTlYqNiOTD9AHHRcdjlssPG1gaMYGNrg10uO+Ki4zg44+ATbQVkjDNycslJwoLCyFc2H5GhkQRfDCYyNJJ8ZfMRFhTGH0v/yHRbUT0cd/7y+XF0dcTG1gZHV0fyl8//VHE/PLZHGQ/uXb/HzeM3uXf9Hh5lPNLmMYmMhDFj4Lnn4NgxWLMG1q174uHiYuL49fNfibofRe5ncuPg4oCNrQ0OLg7kfiY3Ufej+G3qb0+0pdN/n38YTW0Yeern38Nx5yqUi9ioWMLvhBMbFUuuQrmeKu7/3k96bW2V1bZt00oNERERERHJlnx8fGjfvr15pUaJEiU4dOgQ27dvp0KFCsyaNYv8+fPTv39/evbsydatWx851uTJkxk/fnxGhS4iIpLtnFx2ksiQSGwcbIgOi4aHPzu2BVsHWyLuRnBy2Umqda+WqrHv/H2HwLOBxMXGcX7deSJDE7YWcnR1xKOsB7fO3OLO33fIV+bJi1RHh0Wzc9ROgv8JJm/JvDT6tBH2LvZPPF583K5FXYmJiuHSjktE3Y/CIbcDJZqUwLWo6xPHHT92RGgEJxefhIc+/w74JQDPip5P95gcPAjdu8OZM+amYOdncM6TH6fUjwbAPzv/IfRKKE55nYiJieH+lfvExcRhY2dDbq/cOOV1IiQghH92/kOppqmrhxb//MMWou9FJzpvsDc88fMvPm5sIPivYItzD24+wMHN4Ynjjndq5Sl2T9xN8Plg09+OLeQtm5eGoxs+9QqQG8dvcOzbY5xbf47o+9HY57anXOtyVOtRLVOubgIlNUREREREJJs6ePAgP/74I4cOHcLHx4fJkyfTokULDh8+zJtvvmnu99VXX1GyZElCQ0NxfcR+zyNHjmTQoEHm49DQULy8vNJ9DiIiItlFSEAIcbFxkPjzZIiF2NhYsDH1S62IkAhCAkII/jvY4tvrxjgj4XfCuXHkBnlL5SUiJOKJ4/++4/ec+b8zFsmYQ7MO4dPeh9dXvf5EY0aERBATEcPfP/1N5N1Ic3tUaBSnlpzC0d2RgpULPlHcESER3L5wmxD/JB7POAj8I5Co0KjUjx0WBqNHg58fGE3f5o/Fhl9owP7wBsQ22YV7qWN8+NeHqY75/s37xEXHEXot1OJxjouKI/RiKNiCYy5H7t+8n+qxQwJCTM+NRyxAMEYbMRqMT/T8u3/zPlH3o4iNTHqVR1RIFLaOtk8UN5gSGmvfWmuZCIyF4DPBpnaefGurG8dvsLzpcsICw8xtkaGRHP3mKGe+P0OXbV0yZWJD20+JiIiIiEi2tHr1ajp27EjNmjXJnTs3EydO5J9//uHkyZMW/dzd3YmLi+PGjRuPHMvR0RFXV1eLm4iIiKSca1FXi9UCSYr7X79UcsjlQPA/wY/cjicuJo7gf4JxyOWQ6rHhfwmN1ZYJDQBi4czqM3zf8fsnGtfJzYnrv1+3SGg8LPJuJNd/v46TW+rXPtg72yed0HhIiH8I9s6pWGmyezdUqgQzZpgTGtcpzDzeZw8Nif3f9+fv/n2XmaVnpjrm3IVyExkamfhxjhdr+sA9d6HU10TLUzjPIxMaZsb/9Usll3wuj0xoxIuNjMUln0uqx46LiWN9t/XJPibru61/oq2ojHFGVrVeZZHQeFhYYBirWq/KlFtRKakhIiIiIiLZUkxMDP/++6/5+N69ezx48ICBAweyZs0ac/uRI0ewsbHRygsREZF05Onjmab9Hnbn7zvERSX/oW5cVBx3/r6T6rGjw6JNKzT+x2BjMN/inf2/s6YttVLJJb8LMQ9iku0T8yAGl/yp/zD8r61/pWk/jhyBl16Cf/4BIBo7dtCYb+nBLQol6n7377uE3wlPcbwAhaokHudp+j0sMjTpxNGT9ntYSp9XT/L8O7v2LHGRj3luR8Zxdu3ZVI998+RNQgOSrxMXGhDKzZM3Uz12elNSQ0REREREsqV69eqxdu1aZsyYwYoVK2jdujUFCxbknXfeYdSoUezbt49du3bRr18/3nnnHVxcUv+BgYiIiKTMpt6b0rTfw86tP5em/R62c9RO87fkH05kPHxsjDWa+qXS9x1StsIjpf0e9tsXv6VpP557Dl59FYCbeUozh178Rn2M2D7ykhWvrEjZ2P+zvPnyNO33sINfHkzTfg87vvB4mvZ72O6Pd6dpv4ftGrMrTftlJNXUEBERERGRbKlDhw6cP38ePz8/bty4QcWKFVm7di3Vq1fn3LlztGrVijx58tCmTRsmTZpk7XBFRESytUdtcfOk/R72uK1/UtvvYcH/BD++Uyr6PSyl9RuepM5DdHjKVo48st+9e5Dnoa2YDAaYPRuaN2fl5DBC7z2+PkToteRXAfzX/RspqzmR0n4PS+lKmidZcRMdmcKxU9jvYem5wuTBzQdp2i8jaaWGiIiIiIhkSwaDgXHjxnH58mWioqI4duwY1atXB2Dy5MkEBwcTEBDAzJkzyZUrl5WjFRERyd7cS7inab+HFXu+WMKBAdMnng//m1S/FMpbMm+a9nuYm7dbmvZ72BPHbTTCmjXw7LOwcaPluSJFoHdvXIu6p2hs1yKpq4+Su3DKamWktN/D8pXNl6b9HlakZpE07fewfGVSGHcK+z0sv0/+NO2XkZTUEBERERERERERkXT15qY307Tfw6p2q4pD7v8VATeSUBD6oZ8dcjtQtVvVVI/d6NNGxO+w9N+CyfHHBluDqV8qvfH9G2na72Fv//x26vvduAFt20KHDhAYCL17w927ia7pvKlzisZOab94XXd0TdN+D+uyuUua9nvYq7NfTdN+D2sypUma9ntYnQF10rRfRlJSQ0RERERERERERNKVs4cz7qXck+3jXsodZw/nVI9t62BL/Y/qY7D737IMIxYJDYOdgfof1cfW4dH1Hx7F3sUen/Y+5mNjnNF8i1e+fXnsXexTPbaTuxOelZIvjO5ZyRMnd6dUj+2S34XcXsmvaMjtldtUhNxohEWLwMcH1q9P6FCzJkQn3jIpvX6XqYo5ldLz+eeQ24ESL5dItk+Jl0skJN5SwwB2zslXkLBztrNYkZRScbFxOLo7JtvH0d2RuNjkC5Vbg5IaIiIiIpJpXb9+nd9++4179+5ZOxQREREReUof/vXhIz9Ydi/lzod/ffjEYzcY2YCGnzTEKZ+TaWWFAbAF5/zONPykIQ1GNnjisV9f9To+HXz4b01sg60Bnw4+vL7q9Sceu88ffR6Z2PCs5EmfP/o88diDAwY/MkmQ2ys3gwMGw+XL0KwZvPtuwqqMAgVMW1CtXQueSceWXr/LFMX8hNLz+dd1W9dHJjZKvFyCrttSv7oEwMnNCe8G3ti7Jp00s3e1x7uBN05uqU98Obk5UaRmEZwLJp3IcS7oTJGaRZ5o7PRmMBqNxsd3yz5CQ0Nxc3MjJCQEV9fU7esmIiIiIgmaNWtGx44deeeddx7b9++//6ZmzZrcuXMnxeemTZvGxIkT8fLy4vLly2zcuJEXXnghrcI3Wxm6Ms3HzGidXDtZO4Qc9zo7p81XREQkLYXfCWfFKysIvRaKaxFXOm/q/ETfkE9KbFQsJ5edJCQgBDdvN6q8VeWJVmgkJTosmp2jdhL8TzB5S+al0aeNnmiFRlIi7kaw5vU15rjf+P6NJ1qhkZSwoDCWNlnK/Rv3yV04N113dMXFwwm++QZGjIAHDxWC7tIF/PwgX8pqNKTX7zLJmJ9ghUZS0vP5F3U/is0fbObuP3dxL+lOy69aPtkKjf8xxhnZNmgb13+/jmsJV/x3+hN9Pxr73PYUb1Sc0EuhFHmuCC9PexmDTeqWazw8ttuzblz77RpRoVE4uDpQpG4RQi6GPPHYTyqlr7GV1BARERGRVPvuu+/o0qULixYtemxS49KlSzRr1owLFy7w35eejzp34cIFnn/+eY4fP07hwoWZMGECP//8M3v37k3zuSipkTZy2uvsnDZfERERyWZGjoQpUxKOixaFOXOgZUvrxSRJunH8Bns/2UtYUBiuRV2xd7EnOiya0Kuh5Mqfi+fHPk9h38KZbuwnkdLX2Np+SkRERERS5c6dOwwePJiyZcumqH/Lli3p0aNHqs7FxMQwf/58Chc2vYCuUqUKwcHBTx60iIiIiIgk6NMH8uQx/dyzJ/z5pxIamVRh38K8MPYFnqn+DOF3wrnz9x3C74RT5LkiT510SM+x01PyVUZERERERP5j8ODBtGnThvDw8BT137RpEzY2NgwbNizF53x8fPDxMRVkvH//PrNmzaJt27ZPH7yIiIiISE4UEQFOD21n5eUF8+eb6mc0bGi9uCRFCvsWplCVQtz5+w4RIRE4uTnhUcojTbaFSs+x04tWaoiIiIhIiu3evZudO3fy2WefpfiakiVLPtE5gC1btlC4cGFu3rzJqFGjUnyfIiIiIiICREbC2LHg4wMhIZbnOnRQQiMLMdgYyFcmH0VqFCFfmXxpmnRIz7HTg5IaIiIiIpIiERER9OzZk9mzZ2dYDYGXX36ZrVu3Ymdnl+RKDxEREREReYRDh6BaNZgwAS5dAr2elmxCSQ0RERERSZEJEyZQo0YNWmbgXrt2dnbUr1+fL7/8kkWLFmXY/YqIiIiIZFlhYTB4MNStC2fOmNrs7KBgQTAarRubSBpQTQ0RERERSZEVK1YQGBiIu7s7AGFhYaxZs4bDhw/zzTffpPl93bhxg8GDBwOm5IatrW2a3oeIiIiISLazZw/06AEXLya0VasGCxdClSpWC0skLSmpISIiIiIpsn//fmJiYszHQ4YMoXbt2rzzzjuEhobi7OyMvb19mtxXuXLl6NmzJyVLlsTX15dx48bx+uuvp8nYIiIiIiLZTmioaXupuXMT2hwdYfx406oNO30MLNmHns0iIiIikiJFixa1OM6dOzf58+cnf/78FC9eHD8/P1q3bp0m91WtWjVmz57NoEGDuHv3Lu3bt2f69OlpMraIiIiISLYSGwu1a8PZswlt9erBggVQtqz14hJJJwajMWdtpBYaGoqbmxshISEZVuBSRERERDKvlaErrR3CU+vk2snaIeS419k5bb4iIiKSyX39NXzwAeTKBVOmQJ8+YKNyypK1pPQ1tlZqiIiIiIiZPuAXEREREckCYmIst5Tq3RsuXzYlM4oXt1pYIhlB6ToRERERERERERGRrODmTWjXDvr3t2y3sYHPP1dCQ3IErdQQERERERERERERycyMRli6FAYOhOBgU1uHDvDCC9aNS8QKlNQQERERERERERERyawCAuD992HbtoQ2T0948MB6MYlYkbafEhEREREREREREcls4uLgm2+gQgXLhMabb8KZM9CihfViE7GibJHUaNasGYsXL7Z2GCIiIiIiIiIiIiJP78IFePFF6NsX7t83tRUpAps2wfLlkD+/VcMTsaYsn9T47rvv2PZwplJEREREREREREQkqzp2DKpUgf37E9refx/+/BNatrReXCKZRJZOaty5c4fBgwdTtmxZa4ciIiIiOdTt27f57bffCAoKsnYoIiIiIiKSHVStCtWqmX4uWRJ27YK5c8HNzaphiWQWWTqpMXjwYNq0aUPt2rUf2ScyMpLQ0FCLm4iIiEhaWLVqFaVKlaJv3754e3uzatWqx14zdepUChYsiKurK+3ateP27dspOhevY8eO9OvXL03nISIiIiIiVhQXZ3lsYwMLFsDgwfDHH9CwoXXiEsmksmxSY/fu3ezcuZPPPvss2X6TJ0/Gzc3NfPPy8sqgCEVERCQ7u3v3Lv369WP//v0cP36cuXPnMnz48GSv2bdvH0uWLGHfvn0cO3aMiIgIBg8e/Nhz8bZt28auXbuYMGFCus1LREREREQy0OHDppUZv/xi2V6uHHzxBeTKZZWwRDKzLJnUiIiIoGfPnsyePRtXV9dk+44cOZKQkBDz7cqVKxkUpYiIiGRn9+7dw8/Pj4oVKwJQpUoVgoODk73m8OHDtGjRgrJly1KqVCk6derEhQsXHnsOIDw8nD59+jBlyhTc3d3TbV4iIiIiIpIBwsJg6FCoUwdOnYLu3SE83NpRiWQJWTKpMWHCBGrUqEHLFBTGcXR0xNXV1eImIiIi8rS8vLx48803AYiOjuaLL76gbdu2yV5TsWJF1q5dy8WLF7l16xYLFiygSZMmjz0Hptc/4eHh2NnZsWvXLoxGY/pNTkRERERE0s/evaZC4F98kbD1VK5cEBho3bhEsgg7awfwJFasWEFgYKD5W4phYWGsWbOGw4cP880331g3OBERkRzs9u3bnD9/njJlypA/f/6nHu/69ev4+/tTqVIl8uTJkwYRpr2TJ0/SsGFDHBwcOHfuXLJ9mzVrRunSpSlVqhQANWrUYMSIEY89FxAQwPTp06lZsyYBAQHMnDkTb29v1q5di8FgSMfZiYiIiIhImgkNhREjYPbshDZHRxg3DoYMAXt768UmkoVkyZUa+/fv5/Tp05w4cYITJ07w2muv8cknn/DJJ59YOzQREZEcK62LZk+bNo0KFSrQq1cvihYtyt69ey2u/fvvv/Hw8EjzeaRW5cqV2blzJxUqVKBbt27J9l2zZg2XL1/m3Llz3L59m4oVK9KlS5fHnlu8eDEFCxZkx44djB49mj179rB371527NiR7vMTEREREZE08NNPULGiZUKjbl04cQJGjlRCQyQVsmRSo2jRohQvXtx8y507N/nz50+Tb4SKiIhI6qV10ewLFy4wdepUzpw5wx9//MGQIUMYO3as+dpLly7RsmXLx9awyAgGgwFfX18WL17Mhg0bko1p5cqV9O7dm7Jly+Lh4YGfnx9r167l7t27yZ67evUqjRo1wtHREYA8efJQunRpLl26lFHTFBERERGRJzVlCjRvDvG1fl1cYOZM2LfPVBBcRFIlSyY1/mvx4sW888471g5DREQkx0rrotkxMTHMnz+fwoULJzley5Yt6dGjRzrNJmV27drF0KFDzcd2dqZdPW1sHv3yKiYmhn///dd8fOPGDQBiY2OTPefl5UX4Q0UD4+LiuHr1KsWKFUubyYiIiIiISPp57TVwcDD93LgxnD4N/fuDra114xLJorJkTQ0REZHMLq1rS2R2T1o0+4MPPqBnz57kyZPHojC2j48PPj4+ANy/f59Zs2ZZjLdp0yZsbGwYNmxYOs3o8cqVK0fr1q0pXbo0zZs3Z/To0bz88su4ubkRGhqKs7Mz9v9ZQl6vXj2mT59O0aJFcXZ2xs/Pjzp16pAvX75kz73xxhtUr16dH374gVq1ajFr1iwiIyOpV6+elWYvIiIiIiKPZDTCw7XvfHzgs88gTx54913LcyKSatlipYaIiEhmktraEh9//DEGgyHRbc+ePUDydSfidezYkX79+qXHdFLl5MmTFCxYkO3bt+Pn55ds34cLYxcsWJAHDx6YC2PH27JlC4ULF+bmzZuMGjXK3F6yZMn0CD9VnnnmGb7//nv8/PyoUKECYWFhLFu2DDDV2di8eXOiawYMGEDHjh2ZMGEC77//Pm5ubuZrkjtXtmxZVq9ezcSJEyldujSbN29mw4YNmbZ4uoiIiIhIjmQ0wpIlUL8+RERYnhswALp3V0JDJA0YjEaj0dpBZKTQ0FDc3NwICQnB1dXV2uGIiEg2c/fuXUqXLs3u3bupWLEiy5YtY/To0Vy+fPmR10RERBDx0AvegIAAGjduzF9//cXJkyfp06cPP/zwA7a2tnz44Yd4enqyePFic/9t27bx1ltvceHCBdzd3dNxdo9nNBo5ceIEQ4YMwdXVlXXr1j2y75o1a/j4449Zt24dnp6eDBkyhJCQEH744Qdzn5iYGA4ePEi/fv148cUXmTFjhvmcv78/JUqUIIe9lEl3K0NXWjuEp9bJtVOq+ufEOaeHnPY6O6fNV0RERB4jIAB69jQVBAf46CP49FPrxiSSxaT0Nba2nxIREUlDT1JbwsnJCScnJ/PxsGHDGDhwIG5ubhZ1JwA6derEN998Y+4bHh5Onz59mDJlitUTGmBZNLtYsWIEBweTN2/eJPs+XBgbwM/PDzc3N+7evWuei52dHfXr1+fLL7/k1VdftUhqZAR92C0iIiIiIsmKi4O5c2HYMLh/P6H96tXE21CJSJrQ9lMiIiJp6ElqSzzs+vXrrFu3zryVVMWKFVm7di0XL17k1q1bFnUnACZMmEB4eDh2dnbs2rXLaqsW0rpo9ooVK5g2bZrFeLYqoiciIiIiIpnJX39Bw4bQp09CQuOZZ2DjRtM2VEpoiKQLrdQQERFJBydPnqRhw4Y4ODhw7ty5FF83Z84cOnfuTO7cuQHLuhMANWrUMNedCAgIYPr06dSsWZOAgABmzpyJt7c3a9euxZDBL57Tumh2uXLl6NmzJyVLlsTX15dx48bx+uuvZ+icREREREREkhQTA35+MGaMZe2M996Dzz+HTLCKXiQ700oNERGRdFC5cmV27txJhQoV6NatW4quiY2NZf78+fTq1cvctmbNGi5fvsy5c+e4ffs2FStWpEuXLgAsXryYggULsmPHDkaPHs2ePXvYu3cvO3bsSJc5JSeti2ZXq1aN2bNnM2jQIHx9fSlWrBjTp0/P0DmJiIiIiIgkEhtrWp0xdGhCQqNECdi5E+bNU0JDJANopYaIiEg6SE1tiXi7d+8mf/78lC9f3tyWXN2Jq1ev0qhRIxwdHQHIkycPpUuX5tKlS+k3sWQ0bdqUM2fOJGr39/dPsr+TkxNffvklX375ZZLnu3TpYk7gJKV48eIqEi4iIiIiIhnL1haaNIFffjFtL/XhhzBxIuTKZe3IRHIMJTVERETS0K5du9i6dStTp04FUlZbIt6aNWto06aNRVtydSe8vLwskghxcXFcvXqVYsWKPfU8/ksFs0VERERERP5nxAg4fRoGDoQ6dawdjUiOo6SGiFjN7du3OX/+PGXKlCF//vzWDifD5NR55xRPUlsi3k8//cSSJUss2pKrO/HGG29QvXp1fvjhB2rVqsWsWbOIjIykXr16GTFVERERERGR7C08HMaNg9y5YezYhHYHB1izxnpxieRwqqkhIlaxatUqSpUqRd++ffH29mbVqlUpvrZjx47069fPom3q1KkULFgQV1dX2rVrx+3bt83nXn31VQwGg/nWuHHjNJtHauXUeeckT1JbAuDixYtcv36dGjVqWLQnV3eibNmyrF69mokTJ1K6dGk2b97Mhg0byJMnT/pOUkREREREJLvbtw+qVIGpU2HCBDh50toRicj/GIw5bDPq0NBQ3NzcCAkJwdXV1drhiORId+/epXTp0uzevZuKFSuybNkyRo8ezeXLlx977bZt23jrrbe4cOEC7v8rvrVv3z769OnDDz/8gK2tLR9++CGenp4sXrwYMH3IvH37dooWLQqAvb09uayw12VOnbdkDzl1+6mcOG/NOWvKDNur5bTX2TltviIiIjnGvXum7aW++SahzcEB5s+Hrl2tF5dIDpDS19jafkokk8hJWxLdu3cPPz8/KlasCECVKlUIDg5+7HXh4eH06dOHKVOmmD/YBzh8+DAtWrQwF1Lu1KkT3/zvxcfVq1cxGo3m+7KmnDrv7EgfgIqIiIiIiGRD27bB++9DQEBCW926sGABlCtnvbhExIK2nxLJBNJ6S6Lkth2aN28ehQsXxt7enpdfftlcdDgjeXl58eabbwIQHR3NF198Qdu2bR973YQJEwgPD8fOzo5du3YRv9CsYsWKrF27losXL3Lr1i0WLFhAkyZNANMH/7GxsRQtWpRcuXLRsWPHFCUS0kNOnbeIiIiIiIhIpnbnDrzzDjRrlpDQcHGBmTNN21ApoSGSqSipIZnO7du3+e233wgKCrJ2KBni7t279OvXj/3793P8+HHmzp3L8OHDU3Tttm3b2LVrFxMmTLBo//333zl16hTBwcEEBwezYcMGAH755RfGjBnDsmXLuHTpEhEREQwZMiTN55RSJ0+epGDBgmzfvh0/P79k+wYEBDB9+nRKlSpFQEAAQ4cOpW3bthiNRpo1a0bp0qUpVaoUBQsW5MGDB4wYMQKACxcuUL16dbZt28bRo0fx9/fno48+yoDZPVpOnHda/11fv36d3377jXv37qXJeCIiIiIiIpJDnT4NPj6wZElCW6NGcOoU9O8PtrbWi01EkqSkhmQqab1i4VGrEj7++GOLlQzxtz179qTldFIkrbckenjbIXd3d9zd3c11FM6fP8/s2bNp3LgxRYsWpVu3bhw9ejRd5pUSlStXZufOnVSoUIFu3bol23fx4sUULFiQHTt2MHr0aPbs2cPevXvZsWMHa9as4fLly5w7d47bt29TsWJFunTpAsCIESPYunUrFSpUoHz58nz22Wf83//9X0ZM75Fy2rzT+u962rRpVKhQgV69elG0aFH27t1rcf7vv//Gw8MjTWIXERERERGRbK5MGYjfBtzV1VQ7Y8cOKFnSunGJyCMpqSGZRlqvWEhuVcKIESPMqxiCg4M5efIknp6e+Pr6psvckpPWWxIlt+1Q9+7dLcY+f/48pUqVSodZpYzBYMDX15fFixezYcOGZJM5V69epVGjRjg6OgKQJ08eSpcuzaVLl1i5ciW9e/embNmyeHh44Ofnx9q1a7l7926icdzd3QkKCiIyMjK9pvVYOWneaf13feHCBaZOncqZM2f4448/GDJkCGPHjjWfv3TpEi1bttRWWyIiIiIiIpIyDg6wcCG0agVnzkCPHmAwWDsqEUmGkhqSaaT1ioXkViU4OTmZVzG4u7vz1VdfMXDgQNzc3NJlbimRVlsSpXTbodu3bzN37lz69OmTTjN6tF27djF06FDzsZ2dHQA2No/+T5KXlxfh4eHm47i4OK5evUqxYsWIiYnh33//NZ+LX5ETGxtL+/btOXjwoPnckSNHKFSokDlJkJFy4rzT+u86JiaG+fPnU7hw4STHa9myJT169EjbSYiIiIiIiEj2cOUKtG5t2nLqYTVrwvr1UKSINaISkVSys3YAIvHSasVCw4YNMRgMdO/e3aLfo1YlXL9+nXXr1nHp0qW0mcgTit+SaMiQIXTr1o1169Y9su/DWxI5Ojry4YcfUqxYMXbs2MGIESPMdRUAPvvsM9q3b8/s2bMtxujTpw9169alZcuW6TanRylXrhytW7emdOnSNG/enNGjR/Pyyy/j5uZGaGgozs7O2NvbW1zzxhtvUL16dX744Qdq1arFrFmziIyMpF69epw4cYLp06dTtGhRnJ2d8fPzo06dOuTLl4/KlSszcOBA/Pz8CAwMZMyYMVZJ5OTUeaf137WPjw8+Pj4A3L9/n1mzZlmMt2nTJmxsbBg2bFj6TEhERERERESynrg4mDcPhg2De/fg+nX47Tew00ejIlmR/nIl0zl58iQNGzbEwcGBc+fOJds3fsVCzZo1CQgIYObMmXh7e7N27VoMDy0VjF+VsHz58kRjzJkzh86dO5M7d+40n0tqPLwlUbFixQgODiZv3rxJ9k1uS6L/enjbofj+CxcuZN++fZw4cSLd5pOcZ555hu+//56BAwcyZMgQmjZtyrJlywBTcsfPz4/WrVtbXFO2bFlWr17N6NGjOXfuHM8++ywbNmwgT548DBgwgOvXrzNhwgSCgoKoU6cOCxYsAGDkyJFcvnyZJk2aUKBAAXr37s3IkSMzespAzp03pP3f9ZYtW+jQoQPFixdn1KhR5mtLliyJv79/ek5FREREREREspK//zZtKfVwPcZr1+DSJShd2npxicgTMxjjN+LPIUJDQ3FzcyMkJARXV1drhyNJMBqNnDhxgiFDhuDq6prsioVPPvmEBQsWcOHCBRwdHbl37x7FihVj1apVvPzyy+Z+HTp04P79+2zevNni+vjaE7t27aJ8+fLpNqfk7Nq1i61btzJ16lTAtIVQkSJFCA4OfuR2WBMmTODMmTOsXLkSMG1J5OXlxYIFC/j2228ZMmQItWvXBuDbb79lzJgx5q2JDh8+TOPGjfnxxx954YUXMmCGImn/dx0TE8PBgwfp168fL774IjNmzDBf7+/vT4kSJUjP/72tDF2ZbmNnlE6unVLVPyfOGXLmvDXnrOlJnt9pLae9zs5p8xUREclyYmPBzw/GjIGHtnWmRw+YOhUe2upYRDKHlL7G1koNyXTSesVCcqsSdu/eTf78+a2W0IC035Lo8OHDj9x26N9//+XVV19l+PDhVK9enfv37wOkyyqVnPgBUU6cc0ql9d+1nZ0d9evX58svv+TVV1+1SGqIiIiIiIhIDnf6NHTvDocPJ7QVLw7z50PjxlYLS0TShgqFS6aR1kWUwbQqYcCAAaxatYqCBQsmun7NmjW0adMmrabwROK3JPLz86NChQqEhYVZbEn039UlkLAl0cSJEyldujSbN282b0k0cuRIfHx8aNKkCQMGDLDYdmjlypXcunWL0aNHkydPHvNNJL2k9d/1ihUrmDZtmsV4tra26RC5iIiIiIiIZEnz50O1agkJDYMBPvwQTp1SQkMkm9BKDck00nrFQkpWJfz0008sWbIkQ+eZlKZNm3LmzJlE7cnVBmjZsmWSRb7t7e1ZsGCBubbCwwYMGMCAAQOeJlSRVEnrv+u//vqLnj17UrJkSXx9fRk3bhyvv/66lWYnIiIiIiIimU7FihATY/q5bFlYuBDq1rVuTCKSppTUkEwjrYsoL1iwwLwqYfTo0eZr4vfZv3jxItevX6dGjRrpOi9tSSQ5WVr/XVerVo3Zs2czaNAg7t69S/v27Zk+fboVZiYiIiIiIiKZUp06MHgw2NvD2LHg5GTtiEQkjSmpIZlKWq5YeNyqhGeffZaY+My9iKSbtPy7BujSpQtdunR55LXFixdP1yLhIiIiIiIikkns3w9z58KSJfDw1sRTp1ovJhFJd0pqSIbSqgWR7CU7/E2D/q5FRERERESylHv3YORI+Ppr03H16jBwoHVjEpEMo0LhIiIiIiIiIiIikjVs326qmxGf0ADYvBm0Yl8kx1BSQ0RERERERERERDK34GDo1g2aNoWAAFObiwvMmAHbtoHBYN34RCTDaPspERERERERERERybzWrYM+feDmzYS2l16C+fOhZEnrxSUiVqGkhoiIiIiIiIiIiGQ+cXHQuTOsXp3Q5uoKX3wBPXpodYZIDqWkhoiIiIiIiIiIiGQ+NjZQoEDC8SuvwOzZULSo9WISEatTTQ0RERERERERERHJnCZNgueeg+++g40bldAQEa3UEBERERERERERESuLizPVyDAY4P33E9pz54bDh7XVlIiYKakhIiIiIiIiIiIi1nPxoqlGxp494OICTZpAiRIJ55XQEJGHaPspERERERHJtpYtW4a3tze5c+emcePG+Pv7A3D69Glq1KhB3rx5GTp0KEaj0bqBioiI5ESxsTB9OlSqZEpoAISFwYYNVg1LRDI3JTVERERERCRbunjxIqNGjWL9+vWcOXOGYsWK8c477xAZGcmrr75K9erVOXr0KGfOnGHx4sXWDldERCRn+fNPqFcPBg+G8HBTW7FisH07DBhg1dBEJHNTUkNERERERLKl48ePU7t2bapVq4a3tzfdunXjwoULbN26lZCQEKZPn86zzz7LpEmTWLBgQbJjRUZGEhoaanETERGRJxAdDRMmgK8vHDpkajMYoF8/OH3atPWUiEgylNQQEREREZFsycfHh127dnH8+HFCQkL4+uuvadKkCSdPnqR27dq4uLgAULlyZc6cOZPsWJMnT8bNzc188/LyyogpiIiIZC8XL8Jzz8HYsabkBkDZsrBvH3z5pakouIjIYyipISIiIiIi2ZKPjw/t27enWrVquLu7c+jQIb744gtCQ0Mp8VDxUYPBgK2tLcHBwY8ca+TIkYSEhJhvV65cyYgpiIiIZC8FCsDdu6afbW1hxAg4cQLq17dmVCKSxSipkcndvn2b3377jaCgoBRfc/36dX777Tfu3buXjpGJiIiIiGRuBw8e5Mcff+TQoUPcu3ePTp060aJFC+zs7HB0dLTo6+TkRFhY2CPHcnR0xNXV1eImIiIiqZQnD8ybB1WqmLaemjwZnJysHZWIZDFKamRiq1atolSpUvTt2xdvb29WrVr12GumTZtGhQoV6NWrF0WLFmXv3r2J+kRHR1OpUiX27Nljbtu7dy/ly5cnf/78TJ8+PS2nISIiIiJiFatXr6Zjx47UrFmT3LlzM3HiRP755x88PDwIDAy06Hvv3j0cHBysFKmIiEg2dP++qQj45cuW7U2bwu+/Q/Xq1olLRLI8JTUyqbt379KvXz/279/P8ePHmTt3LsOHD0/2mgsXLjB16lTOnDnDH3/8wZAhQxg7dmyifp9//jmnT582HwcGBvLaa6/RqVMnDhw4wHfffcfu3bvTfE4iIiIiIhkpJiaGf//913x87949Hjx4gJ2dHQcPHjS3+/v7ExkZiYeHhzXCFBERyX527ICKFWH6dHj/fTAaLc/b2lonLhHJFpTUyKTu3buHn58fFStWBKBKlSrJ7vELpjdt8+fPp3Dhwo+85q+//uKLL76gePHi5rbvvvuOwoULM2bMGEqXLs3YsWNZsGBB2k5IRERERCSD1atXj7Vr1zJjxgxWrFhB69atKViwIP379yckJISlS5cCMGXKFBo3boytPmARERF5OsHB8O678PLLCSs09u+Hc+esG5eIZCt21g5Akubl5cWbb74JmLaL+uKLL2jbtm2y1/j4+ODj4wPA/fv3mTVrVqJrevbsyYgRI9i6dau57eTJk7z00ksYDAYAatasyciRI9NyOiIiIiIiGa5Dhw6cP38ePz8/bty4QcWKFVm7di329vbMmzePzp07M3ToUGJjY5PctlVERERSYf166NMHbtxIaGvYEObPh2eftVpYIpL9aKVGJnfy5EkKFizI9u3b8fPzS9E1W7ZsoXDhwty8eZNRo0aZ2xctWkRISAiDBw+26B8aGkqJEiXMx66urly7di1N4hcRERERsRaDwcC4ceO4fPkyUVFRHDt2jOr/27+7devW/PXXX8ybN4+zZ89SoUIFK0crIiKSRd26BR06QJs2CQmNPHlg7lzYuVMJDRFJc0pqZHKVK1dm586dVKhQgW7duqXompdffpmtW7diZ2fHsGHDAFPdjJEjR7JgwQLs7CwX6NjZ2eHo6Gg+dnJyIiwsLO0mISIiIiKSCRUpUoRWrVrh6elp7VBERESypvXrwccH1qxJaGvZEs6cMdXS+N+uICIiaUlJjUzOYDDg6+vL4sWL2bBhw2PraoApSVG/fn2+/PJLFi1aBMCAAQPo3r07VatWTdTfw8ODwMBA8/G9e/dwcHBIszmIiIiIiIiIiEg25OAAt2+bfvbwgOXL4ccfoWhR68YlItmakhqZ1K5duxg6dKj5OH51hY3No39lK1asYNq0aRbXxBc7XLFiBbNmzcLd3R13d3d++eUXXnnlFaZMmUKNGjU4ePCg+boTJ05QpEiRtJ6SiIiIiIiIiIhkJy1aQJcu8PrrptUZb76p1Rkiku5UKDyTKleuHK1bt6Z06dI0b96c0aNH8/LLL+Pm5kZoaCjOzs7Y29snuqZnz56ULFkSX19fxo0bx+uvvw7ApUuXLPp27NiRAQMG0KxZM2JiYujbty+7d++mQYMGfPHFFzRt2jTD5ioiIiIiIiIiIpncxYuweDF88oll4mLhQvjPZ1QiIulJKzUyqWeeeYbvv/8ePz8/KlSoQFhYGMuWLQNMdTY2b96c6Jpq1aoxe/ZsBg0ahK+vL8WKFWP69OkAFC9e3OLm5OREoUKFcHd3J3/+/EybNo2mTZtSuHBhTp8+zejRozN0viIiIiIiIiIikgnFxsKMGVCpEkycaNpi6mFKaIhIBtNKDStZGbry8Z3qwJiDY8yHP/MzhMLkPyYTTniSY9i+Zsuk1yaZjzfEbIDQxEP33NiTG9wwj5G3S14+q/MZ1y5co3y98uxy3pXkdf/VybXT4zuJiIiIiIiIiEjWc+YMdO8OD21bzowZpi2ntM2UiFiJVmqIWaFnC1G9eXVcXF2sHYqIiIiIiIiIiFhLdLRpVYavr2VCo29f2LtXCQ0RsSqt1BARERERERERERGT48fh3XfhxImEttKlYcECaNDAamGJiMTTSg0REREREREREZGcLi4OPvoIatRISGjY2MDw4XDypBIaIpJpaKWGiIiIiIiIiIhITmdjA/7+psLgAJUrm1ZnPPecVcMSEfkvrdQQERERERERERERmDkTihSBTz6BI0eU0BCRTEkrNURERERERERERHKan3+GBw+gVauENk9P+OsvcHa2XlwiIo+hlRoiIiIiIpIpRUVFWTsEERGR7OfuXejRA5o0ge7d4dYty/NKaIhIJqekhoiIiIiIZErdu3dn2LBh1g5DREQk+9i4ESpUMNXKALh9G+bOtW5MIiKppKSGiIiIiIhkOtu3b+e7774jLi7O2qGIiIhkfYGB0KmTaaup69dNbXnywJw5MGqUdWMTEUkl1dQQEREREZFM5dKlS7z11lvUqFGD9957j5iYGOzs9NZFREQk1YxGWLUK+veHoKCE9hYtTAkNLy/rxSYi8oT0zkBERERERDKNs2fP0rx5czw8PJg7dy4+Pj4YDAYKFSqEl5eX+Va7dm1ef/11a4crIiKSed28Ce+/Dz/+mNDm4QEzZ8Kbb4LBYL3YRESegrafEhERERGRTOHrr7/mueeeo0CBAuzevRtPT0+MRiOLFy9m2LBhNGjQAHt7e/bt20fHjh2ZNm2atUMWERHJvGxs4LffEo5ffx3OnIEuXZTQEJEsTSs1RERERETEqk6cOEHnzp25fPkyI0aMYOTIkdjZ2XHt2jUMBgNt27bFxcXF4pp3332XpUuXMnjwYCtFLSIikskVKGBalTFkCHzzDbRpY+2IRETShFZqiIiIiIiIVXl6evLCCy9w/vx5xowZk6L6GSVLlqREiRIZEJ2IiEgWEBtrSlw8XDcDoHNnOH9eCQ0RyVa0UkNERERERKyqSJEizJ49+5HnDUlskTF48GCcnZ3TMywREZGs4exZ6N4dDhyAX3+F775LOGcwgKur9WITEUkHWqkhIiIiIiJWFx0dnWS70WjklVdeYdCgQWzatImYmBgAJTRERESio2HSJKha1ZTQAFixAk6ftmpYIiLpTUkNERERERGxugYNGtCrVy9u3ryZ6FyhQoXYv38/rVu3pnjx4qxfvz7jAxQREclMjh+HmjVh1CiIijK1lS4N+/ZBxYrWjU1EJJ0pqSEiIiIiIlYVHR1N69atWbduHWXKlGHy5MnExsYSExODwWBgzpw5HDlyhEuXLvHSSy/Rvn175syZY+2wRUREMl5EhCmRUaMGnDhharOxgWHD4ORJaNDAquGJiGQEJTVERERERMSq7O3tGTFiBP7+/gwbNozx48dTt25dzp49i4ODA1H/+waql5cXS5cuZejQofTr149ff/3VypGLiIhkoIMHwdfXtOVUbKyprVIlOHQIPvsMtDWjiOQQSmqIiIiIiEim4OzszOjRozl8+DARERG89dZbbN++nXz58ln0mzx5Mk2bNqV3797ExcVZKVoREZEMduECnDtn+tneHj7+GI4eheees2pYIiIZTUkNERERERHJVCpXrsyBAwd47rnnaNasGadOnUrUZ8KECYSFhREYGGiFCEVERKzgrbegWTPT1lPHjsG4ceDgYO2oREQynJIaIiIiIiKS6bi4uLBhwwb69++Pj49PovO+vr4cOHCAggULWiE6ERGRdHb3LixaZNlmMMCKFfDbbyoGLiI5mpIaIiIiIiKSKTk4ODB58mRsbW2TPO/p6ZnBEYmIiGSAH3+EChXg3XdhyxbLc3nzgp2ddeISEckklNQQEREREZFM7cqVKwwYMMDaYYiIiKSvwEDo3Bleew2uXze1DR0Kqh8lImIhSyc1bt++zW+//UZQUJC1QxERERERkScUFhZGmTJluHr1Kl999RUdOnTg8OHD5vMhISEsWLDAihGKiIikI6MRVq0CHx9YuTKhvXlz+OknsMnSH9+JiKS5LPtfxVWrVlGqVCn69u2Lt7c3q1atsnZIIiIiIiLyBOzt7fn7778xGAzExsayZ88e6tSpQ61atfj555+xt7fHTlttiIhIdnTtGrRuDZ06QfyXdvPmhaVLYfNm8PKyangiIplRlkxq3L17l379+rF//36OHz/O3LlzGT58uLXDEhERERGRJ2Bvbw+Ara0tH374ITdu3GDDhg3kzZsXg8EAYP5XRERyNmOckdsXbnPtyDVuX7iNMc5o7ZCejNEICxaYamds3JjQ3r49nD0Lb71lKgwuIiKJZMmvO927dw8/Pz8qVqwIQJUqVQgODrZyVCIiIiIi8jTiC4Lb2Njwyiuv8MorrwBw/vz5R14TERGBk5NThsQnIiLWdeP4DU4uOUng2UBiImKwc7LDs7wnVd6uQmHfwtYOL/XWrIGQENPPBQvCN99A27bWjUlEJAvIkkkNLy8v3nzzTQCio6P54osvaPuI/+hHRkYSGRlpPg4NDc2QGEVEREREJHUKFSqU7Pn4pMd/xcbGpkc4IiKSidw4foO9n+wlLCgM16Ku2OeyJ/pBNNd/v87dy3d5YewLWSuxYTDA3LlQqZIpkTFjBnh4WDsqEZEsIUsmNeKdPHmShg0b4uDgwLlz55LsM3nyZMaPH5/BkYmIiIiISGotWbKEvHnzJmq/cuUKw4YNY+XDxVMxfcHp4S8wiYhI9mSMM3JyyUnCgsLIXz6/eUtCR1dH8pfPT9DZIP5Y+geFqhTCYJNJt2w6dw7u3IG6dRPaihc3tRcpYrWwRESyoiyd1KhcuTI7d+5kyJAhdOvWjXXr1iXqM3LkSAYNGmQ+Dg0NxUtFlkREREREMp0mTZpQsGBBAGJiYtixYwfNmzfn/Pnz2NnZ0bJlSytHKCIi1nDn7zsEng3EtahrohpLBoMB16Ku3Dpzizt/3yFfmXxWivIRoqPhiy9g/HjTFlOnT0OePAnnldAQEUm1LFkoPJ7BYMDX15fFixezYcOGJOtqODo64urqanETEREREZHMIy4uDoPBQFxcHGFhYUybNg1vb29eeeUVNm3aBIDRmEULwYqIyFOLCIkgJiIG+1z2SZ63d7EnJiKGiJCIDI7sMU6cgFq14KOPIDISAgJMCQ4REXkqWTKpsWvXLoYOHWo+trMzLTixscmS0xERERERydEiIyMxGo1ERUXxzTff8Nlnn9G9e3dOnDjBK6+8QlRUFFFRUYBpBYeIiOQsTm5O2DnZEf0gOsnz0WHR2DnZ4eTmlMGRPUJkJIweDTVqwPHjpjYbGxgyBIYPt25sIiLZQJbMApQrV465c+cyb948rly5wogRI3j55Zdxc3OzdmgiIiIiIpJKTk5OHD9+nGeeeYY+ffoQEBDAhAkTqFSpEgDOzs40btyYsLAwKlasyIIFC6wcsYiIZCSPUh54lvck9GpoopV7RqOR0KuhFPApgEepTFBo++BB8PWFTz+F+ER8xYqm9qlTwcXFuvGJiGQDWTKp8cwzz/D999/j5+dHhQoVCAsLY9myZdYOS0REREREnoDBYKBKlSqsXr0aBwcHnJwsv2lbqlQpNmzYgIuLC5MnT6Z///5s377dStGKiEhGM9gYqPJ2FVzyuxB0NojI0EjiYuKIDI0k6GwQufLnonLXytYtEv7gAQwcaCoEfvasqc3eHsaNg99/N63aEBGRNJFlC4U3bdqUM2fOWDsMERERERFJA3369GHRokWUKlWK2rVrU7duXezt7XFwcEhUFDZXrly0bduWrVu30qBBAytFLCIiGamwb2FeGPsCJ5ecJPBsIPeu38POyY4izxWhctfKFPYtbN0Ab92CefMgfiXJc8/BwoXwv1WHIiKSdrJsUkNERERERLKHKVOmsHz5crZt20bt2rUBOHjwIH379iV//vyJ+lerVo3ly5crqSEiksMU9i1MoSqFuPP3HSJCInByc8KjlId1V2jEK1ECJk821cyYMAEGDAA7fewmIpIe9F9XERERERGxqvfff59GjRpR4z9bc/Tp04fy5csnec3o0aPJnTt3RoQnIiKZiMHGQL4y+awdBmzdCg0awMP/L/rgA3j1VVOCQ0RE0k2WrKkhIiIiIiLZh4eHh0VCIyIigoIFCxIVFfXIa5TQEBERqwgKgjffhBYt4KOPLM/Z2CihISKSAZTUEBERERGRTMXJyYkbN25QpUoVa4ciIiJiYjTC6tXg4wMrVpjavvoK/vjDunGJiORASmqIiIiIiIiIiIg8yvXr0KYNdOwIgYGmtrx5YdEiFQIXEbEC1dQQERERERGrO3LkCE5OTtjb22Nj8/jvXjk4OFC8ePH0D0xERHIuo9GUuBg0CEJCEtrbtoWvv4ZChawXm4hIDqakhoiIiIiIWF2tWrUwGAypusbHx4djx45hb2+fTlGJiEiO5e8P778PO3YktBUoYEpmtG9vtbBERERJDRERERERySROnz6Ng4MDRqORihUr8ueffwLw4MEDqlatyl9//WXue/XqVRo2bMju3bt5+eWXrRWyiIhkV+vXWyY03noLZsyAfPmsFpKIiJhkqqRGeHg4Z8+epVq1atYORUREREREMpDBYODZZ5/FwcEBABsbG5599lnAlNSIPx+vePHivPbaa+TNm9cq8YqISDbXrx+sWgXXrsHcudCihbUjEhGR/8mQQuElS5Y0f8sq3qRJkwiML670P2fOnKFGjRoZEZKIiIiIiGRhtra2rF+/Xu8fRETk6cXEwM6dlm22trB6Nfz5pxIaIiKZTIYkNfz9/YmMjLRoGzNmDDdu3MiIuxcRERERkSwgtTU1REREntrJk1CrFjRpAgcOWJ4rVgxcXa0Tl4iIPJLVtp8yGo3WumsREREREclkjEYjZcqUMSc2IiMjKVmypPkcYD4GiIuLIzIykoMHD1KsWLGMD1hERLK2yEiYOBGmTDGt1ABTYfCTJ8EmQ74DLCIiTyhT1dQQEREREZGca8CAAdjZ2WE0Ghk0aBCDBw8GTAmOoUOHmo8BYmNjiYyMxN3d3UrRiohIlnXwIHTvDmfOJLRVqAALFiihISKSBWRYUuPBgweEhoYCCd+0un//vrkt/lhERERERHIeg8FA7969zYXChw0bRt++fQHTe4mhQ4eaj0VERJ5IWBiMHg1+fhC/g4idHXz0kenm6GjV8EREJGUyLKnx4osvJmpr0KCBxbHRaNQ+uiIiIiIiIiIikrZ274YePeCffxLaqleHhQuhcmXrxSUiIqmWIUmNRYsWZcTdiIiIiIhIFqYvOImISLowGmHkyISEhqMjfPIJDBpkWqkhIiJZSob8l/vtt99OUb+YmBh+/fXXdI5GREREREQym/8WCo+IiLAoFG40GqlZsyYVKlTgxRdf5NVXX8XDw8OaIYuISFZhMMC330K1alCrlql2Rpky1o5KRESekNXT0efOnWPHjh1s376dvXv38uDBA2JjY60dloiIiIiIZKDZs2djb2+PnZ0dNjY2GAwGjEYjcXFxREdHExYWxs2bNzl79iyDBg2iZ8+etGnThlGjRlGxYkVrhy8iIplJUJDpVq5cQlvFivDrr6Ytp1QMXEQkS8vwpMbt27f5+eef2bFjBzt27ODq1avY2dlRvXp1+vTpw/PPP5/RIYmIiIiIiJX17NkzxX1jY2NZt24d48eP58yZM0pqiIiIidEI338PH3wABQrA779bFv+uUcN6sYmISJrJkKTGnj172L59Ozt27OD48eMYDAaqVq3K66+/zowZMzhw4ADVqlXLiFBERERERCSLs7W1pX379rRp08baoYiISGZx4wb06QPr15uOAwPh889hzBirhiUiImkvQ5IaL730EgaDAUdHR4YOHcqQIUPIly8fANOnT8dORZlERERERHK8uLg4bt68yTPPPJOi/ra2tukckYiIZHpGIyxZAgMHwt27Ce2tW0OPHtaKSkRE0lGGbCK4bt06+vTpg5eXF5999hne3t40btyYCRMmmAsBioiIiIhIznbjxg1KlCgBwM2bNwG4fv36U425ePFiDAZDotvixYvp16+fRVupUqWeeg4iIpKBLl+G5s2hW7eEhIanJ6xZA2vXQuHCVg1PRETSR4YskWjVqhWtWrUC4PLly+Z6Gl9++SVGo5H69etTp04dGjRoQP369XnxxRczIiwREREREclkHBwcAKhZsyYXL17Ey8uLqKgobG1tyZMnD46OjtjY2BAXF0dERAT3799PdrzOnTvTunVr8/H9+/fx9fXl+eefZ968eWzevJm6desCWvkhIpJlxMXB7NkwYgQ8/P+BLl1gxgzIn996sYmISLrL8H2fihUrRo8ePejRowdGo5Fjx46Z621MnDiRmJgYYmJiMjosERERERHJBGxsTIvJc+XKhZ2dHUaj0ZxssLW1ZfXq1RiNRl5++WV+/PHHx47n4OBgTpQAfPPNN7Rt2xZvb29Onz7N888/T+7cudNnMiIikj7++gsGDID4z4+KFoU5c6BlS6uGJSIiGSNDkhrvvvvuY/sUL14cT09P9u3blwERiYiIiIhIZhSf1HB0dMRgMJiPwZSgaNSoEQAGg4GGDRumauyIiAhmzpzJoUOH+OOPPzAajVStWpVr167xwgsvMG/ePLy9vZO8NjIyksjISPNxaGhoaqcmIiJppWxZGDkSJkyAnj1NBcFdXa0dlYiIZJAMqamxePFilixZwpIlSzh37hyxsbEAGI1Gi5uLiwvNmjXLiJBERERERCQT8ff358CBA+bj+GTGo2rwGY3GVN/HihUrqF27NsWLF+fs2bNUqFCBlStXcubMGezt7enZs+cjr508eTJubm7mm5eXV6rvX0REntCff0JUlGXbqFHw/+zdd3gUVdsG8HtbKiQhEEoglBCkSUIviSBIVUBAkd6kKgivgkpHEQWk80nvRRRRFJXeBAXpJRAShICUQICElE3bzWb3+f5Ys0koSkky2eT+ee1F5sxkfM7Mmd3NPHPO+eMPaw8NJjSIiAqUXOmpMWPGDJw/fx7BwcE4ffo0zpw5g+rVq8Pf3x81atRAjRo14O/vj+LFi+dGOERERERElIccP34cQUFBeOutt2xJjPSkRebkxbMkMjJbsmQJJk+eDADo2bMnevbsaVu3YMEC+Pr6Qq/Xw+0RN8fGjh2LkSNH2pb1ej0TG0REOc1oBL74Apg2DZg4EZg0KWOdoyPw0kvKxUZERIrJlaTGhx9+aPs5LS0NYWFhOHfuHIKDg7Fz507b+mLFisHf3x979uzJjbCIiIiIiCgPqFu3Ls6ePQt3d3fs2LEDwPMnMB4UHh6O8PBwtGjR4pHrPTw8YLFYEBkZ+cikhqOjIxwdHbM1JiIi+hfHjgEDBlh7aQDA558DnTsD1aopGxcRESkuVycK1+v1OHfuHM6dO4ezZ8/i3LlzCAkJgVqthp+fn63XBhERERERFRxqtRrVq1fHrVu3YLFYAACpqakwm80P9dS4efMmLBYLVCoVbt68icqVKz/R/2PTpk1o164ddDodAGDkyJFo2LAhunTpAgA4ceIE1Go1e18QESktOdnaK2PePOCfzwRotcCYMUDFioqGRkREeUOuJDU6duyI4OBg3LhxA6VKlbIlL4YNG4YaNWqgWrVqcHBwyI1QiIiIiIgoD0tPaiQlJcFkMkFEYDabodFoEBMTg/LlywOwJjiqVatmm6/vv+zcuRNvv/22bblmzZoYP348SpYsibS0NAwfPhz9+vWDi4tLtteJiIie0MGD1t4ZV65klNWuDaxaBQQEKBcXERHlKbmS1Pjll19sP+t0Oly8eBEXL17EDz/88Mjtr169mhthERERERFRHpP6z0Swa9asgU6nw44dO2yThoeHh8PJyQlqtRoWiwVGo/GJ9pmSkoJjx45h2bJltrI+ffogLCwMHTp0QOHChdGpUydMnTo1+ytERET/Ta8HRo+2TvqdztERmDwZGDXK2lODiIjoH7nyqbB69erc+N8QEREREZGdGjNmDAYPHmybX69Zs2YAgNatW9u28fX1faZ9Ozs7PzIBMm3aNEybNu2Z9klERNlo4cKsCY2gIGDlSuAJhxgkIqKCJVeSGn379s2N/w0REREREdmhCxcuYM6cOZg7dy5ef/11bN269T9/R0RgsVhgMBiwcOHCXIiSiHKDWAQx4TEwxBvg5O4ETz9PqNQqpcNSTIE5Hh98AKxbB9y8CUyfDgwdCvzTS4+IiOhB7L9HRERERESKql69Ou7evYvNmzdj3rx52Lx5M3Q6HTp27PjYOS5MJhMMBgPu3r2by9ESUU6JPBOJ4LXBiAqLQpohDVonLbyqeiGgbwBK1SqldHi5Ll8fj0uXgBdeyFh2cgI2bgTc3YF/5k4iIiJ6HCY1iIiIiIhIcUWKFMHAgQMxcOBAfPfdd/j4449x5MgRLFiwAK+//rrS4VEBVmCelFdY5JlIHPzsIJKjk+FWxg06Vx1MSSbcPnUbcdfj8PKkl+3/Rv5TyLfH484dYNgwYOtW4MwZoFq1jHWcCJyIiJ4Q+/IREREREVGe0rVrV4SGhqJ58+b48ccfISJKh0QFVOSZSOwauQvbh2/H7g93Y/vw7dg1chciz0QqHVq+IhZB8NpgJEcno1jVYnB0c4Rao4ajmyOKVS2G5OhknFt3DmIpGO8F+fJ4iABr11qTGD/+CKSmAgMGAGaz0pEREZEdYk8NIiIiIiLKc1xdXbF69WqICFQqPhVPuS/fPimfB8WExyAqLApuZdweut5VKhXcyrjhXug9xITHoOgLRRWKMvfku+Nx/TowZAiwa1dGmZcX8P77nDeDiIieCT89iIiIiIgoz2JCg5SQL5+Uz8MM8QakGdKgc9U9cr3ORYc0QxoM8YZcjkwZ+eZ4WCzAwoXAiy9mTWj07AmEhgJduwJ8jyciomfAnhpERERERKSo2NhYVK9eHU5OTk/1eyVLlsR3330HHx+fHIqMCqp896R8Hufk7gStkxamJBMc3RwfWm9KNkHrpIWT+9O9R9irfHE8Ll0CBg4E/vgjo6x0aWDJEqBdO+XiIiKifIFJDSIiIiIiUpSLiwumTJkCR0fHJ+6ZYTQaMW7cOKxZswYTJ07M4QipoHmSJ+UTbifk/Sfl7YSnnye8qnrh9qnbKFa1WJb3ARGBPkKP0nVLw9PPU8Eoc4/dHw8RoEsXIDg4o2zQIGDmTMDdXbm4iIgo32BSg4iIiIiIFOXo6IgBAwYAAObMmfPY5EZqaio0Gg2GDx8OADh+/Dh8fX1zNVYqGPLFk/J2RKVWIaBvAOKuxyE6LNo6h4mLDqZkE/QRergWc4V/H3+o1AVjqCK7Px4qFbBgAdCkCVC+PLBiBfDKK0pHRURE+QiTGkRERERElGds2rQJTk5Oj0xqmM1mODs725IaS5Ysye3wqICw+yfl7VCpWqXw8qSXEbw2GFFhUUi4nQCtkxal65aGfx//Ajcpu10dj9RUICYGKFkyo+yll4AffgBatwZcXZWLjYiI8iUmNYiIiIiISHEWiwV6vR779+9/aJ2IwGg0wtHREa7/3BwLCQnBxo0b8fnnn+d2qFQA2P2T8naqVK1SKBlQEjHhMTDEG+Dk7gRPP88Ce5zt4ngcPw707w94eAC//w6o1Rnr3nhDsbCIiCh/U//3JkRERERERDkrLCwMRYsWReHChR96ubm5wcvLC/PnzwcAzJ07F3Xq1MHBgwdx//59hSOn/Cr9SXnvOt5IiUlBTHgMUmJSULpuaTSZ1CRvPSmfj6jUKhR9oShK1yuNoi8UzVs38BWQZ49HcjLw4YdAo0bAhQvA4cPAwoVKR0VERAUEe2oQEREREVGe4O3tjUOHDuHOnTsAgMKFC8PJyQkajQYAUKRIEaxYsQJjx47F3LlzMXToUCXDpQLALp6UJ8ptBw8CAwcC4eEZZbVrA40bKxcTEREVKExqEBERERFRnqDT6VCuXDl88MEH2LJli20eg0KFCqF06dIoW7Ysmjdvju+//x7t27dXOFoqKNKflCcq8PR6YPRoIPN8Ro6OwKefWnttaHmLiYiIcgc/cYiIiIiIKE+ZOnUqvvjiC+h0OgBAVFQUbt26hd9//x2LFy9GREQEBg8ejM8++wyenpyomYgox+3YAQwZAty8mVEWFASsWAFUqaJcXEREVCAxqUFERERERHlCZGQkXnnllceu79q1K+bPn4+vv/4aEydOxM8//4wff/wR9erVy8UoiYgKmL/+Atq2BUSsy66uwLRpwLBhWScGJyIiyiVMahARERERkeK8vb3x1VdfwcnJyTbsVDqLxQKDwYAaNWpApVKhd+/eeO211/DGG2/g2rVrTGoQEeWkypWBd94BFi8GWrQAli8HypdXOioiIirAmNQgIiIiIiLFFSlSBMeOHUPXrl3RokULAEBwcDBKly6NYsWKoU2bNjhw4ADc3NxQrVo1FC1aFHv37rUNUUVERNkkKgooWjRrL4zp04FGjYBevYAHEs9ERES5jf0EiYiIiIhIcYsXL8b69evh6uoKALhx4wbatGmDzz77DGazGZ07d0ZYWBj8/f3x5ptv4siRI0xoEBFlJxFg3Tprz4zMk4EDgJsb0Ls3ExpERJQnMKlBRERERESK2759O7788ks0atQIAPDWW2/B398fc+bMgUajwcCBA3H69Gn8/vvvSEhIQFBQEAIDA5Gamqpw5ERE+cCNG8BrrwF9+wKxscDo0cD160pHRURE9EgcfoqIiIiIiBT3888/Q51pqJMpU6agbt260Gqz/skSGBiI3bt347vvvkNMTAwcHBxyO1QiovzDYgGWLgU+/hhITMwof/1164TgREREeRB7ahARERERkeJSUlLw/vvv25ZbtWoFT0/Px27ftWtX3LhxAzExMbkQHRFRPnT5MtCsGTB0aEZCw9sb+OUXYMMGoFgxZeMjIiJ6DCY1iIiIiIhIcWazGcuWLbMtOzk5oUiRIihevLjtVaRIETg7O+Py5cv44YcfsH79eqSlpSkYNVHBZEmzIHxXOM6uPYvwXeGwpFmUDomeRloaMGsW4O8P/P57RvnAgcCFC0D79srFlo+JRXD/0n3cOnEL9y/dh1hE6ZAUxeNBRM+Dw08REREREZHiHB0d4ejoaFsWEWzZsuWR2xYuXBgjR47E5s2bUbx48VyKkIgA4Py353F4xmHob+phMVmg1qnh5uOGoI+DUKN7DaXDoycxYwYwfnzGcoUKwPLlQPPmysWUz0WeiUTw2mBEhUUhzZAGrZMWXlW9ENA3AKVqlVI6vFzH40FEz4tJDSIiIiIiyhNUKpXtZ41Gg7p16+LcuXOoVKkSimUaBuW7775Dhw4d0KBBAyXCJCqwzn97HrtH7UZqYiqcijhB66hFmjENsVdisXvUbgBgYsMeDBsGLFoE3L4NjBgBfPEF58/IQZFnInHws4NIjk6GWxk36Fx1MCWZcPvUbcRdj8PLk14uUDfyeTyIKDswqUFERERERHlCQkIC3njjDdSsWRMAEBYWhqCgIKhUKmi1WlSsWBGNGjVC//798X//93/KBktUwFjSLDg84zBSE1NRyLsQ1CrraNYOLg7QOmuReDsRf878E9Xfqg61liNd5ylxcYCHR8ayuzuwfj3g6AgEBioVVYEgFkHw2mAkRyejWNVituS9o5sjilUthuiwaJxbdw4lA0pCpVb9x97sH48HEWUXftMgIiIiIqI8IT1xcerUKQBAnTp1EB0djRs3buDMmTP47LPPYDQa8corr+CLL75QOFqiguXqvqvQ39TDqYiTLaGRTq1Sw6mIE+JvxOPqvqsKRUgPSUkBPvoI8PUFbt7Muq5ZMyY0ckFMeAyiwqLgVsYtS29EwNo70a2MG+6F3kNMeIxCEeYuHg8iyi7sqUFERERERIoTETg6OmLmzJkArHNsHDp0CCIZE4cWL14cgwcPRrdu3dC3b1/4+fmhW7duSoVMVKAk3kmExWSB1vHRtxG0jloY44xIvJOYy5HRI/3+OzBgABAebl1+5x1g61ZAxaffc5Mh3oA0Qxp0rrpHrte56JBwOwGGeEMuR6YMHg8iyi5MahARERERkeKMRiMMhqw3Mdq3bw+dLuPGh8ViQWpqKr7//nusWbMGAwYMwKuvvgp3d/fcDpeowClUshDUOjXSjGlwcHF4aH2aMQ1qnRqFShZSIDqySUgAxoyxzpmRzsEBCAoCLBZAo1EutgLIyd0JWictTEkmOLo5PrTelGyC1kkLJ3cnBaLLfTweRJRdmNQgIiIiIiLF6XQ6jBs3zrZsNBoBAOvWrUOzZs3g4+Pz0O/4+/vjyJEjaNOmTa7FSVRQ+Tb3hZuPG2KvxELrrM0yBJVFLDDEGuDp5wnf5r4KRlnA7dwJDB6cdaipRo2AlSuBqlWVi6sA8/TzhFdVL9w+dTvLHBKAtYeiPkKP0nVLw9PPU8Eocw+PBxFlF86pQUREREREinNxccG5c+ewZ8+eLOX9+vVD+fLl8fbbbz/0O0uXLmVCgyiXqLVqBH0cBIdCDki8nYjU5FRYzBakJqci8XYiHAo5IPCjQE4SroSYGKBfP+DVVzMSGi4uwLx5wB9/MKGhIJVahYC+AXAp5oLosGgY9UZY0iww6o2IDouGazFX+PfxLzCTYvN4EFF2YU8NIiIiIiLKEw4fPozdu3djw4YNaN++PUQEGo0Gt27dwt27dx/avmLFigpESVRw1eheAwBweMZh6G/qYYwzQq1Tw9PPE4EfBdrWUy4SAVq1Ak6dyihr3hxYtsw6QTgprlStUnh50ssIXhuMqLAoJNxOgNZJi9J1S8O/jz9K1SqldIi5iseDiLIDkxpERERERJRnrF69GoMHD0aVKlXQoEEDWCwWTJgw4aHtXFxcMHDgQLz44osKRElUcNXoXgPV36qOq/uuIvFOIgqVLATf5r7soaEUlQqYNAno0AFwcwNmz7ZOEM4JwfOUUrVKoWRAScSEx8AQb4CTuxM8/TwLbI8EHg8iel5MahARERERUZ7RtGlTfPzxx/jyyy+xbNkyzJw5E66urg9td+vWLQwePBh//vmnAlESFWxqrRp+rf2UDqNgEgGSk4HM74uvvw7MmQN06QKULq1cbPSvVGoVir5QVOkw8gweDyJ6HkxqEBERERFRnpCamgqj0QiDwYCff/4ZlSpVglqtxty5cx/aNj4+HhUqVFAgSiIihdy8CQwZAmi1wM8/Z+2N8cEHysVFRESUy5jUICIiIiKiPCEgIABqtRpjx44FAKSkpMBsNsNkMkGn02XZ1sHB4aFJxYmI8iWLxTpHxscfAwkJ1rKNG4Hu3ZWNi4iISCFMahARERERUZ6wf//+LMvOzs6IjIx8KKGRvq5OnTq5FRoRkTLCw4GBA4GDBzPKvL0BDw/FQiIiIlIakxpERERERJRnlShRQukQqIATi3Ay21zE4/0PsxmYNw+YOBFISckoHzgQmDmTSY0cwvZHRGQfmNQgIiIiIiIieoTIM5EIXhuMqLAopBnSoHXSwquqFwL6BqBUrVJKh5fv8Hj/IyQEGDAAOH48o6x8eWD5cqBFC8XCyu/Y/oiI7AeTGkREREREREQPiDwTiYOfHURydDLcyrhB56qDKcmE26duI+56HF6e9DJvdGYjHu9/XLoE1K4NmEzWZZUKGD4c+OILoFAhZWPLx9j+iIjsi1rpAIiIiIiIiIjyErEIgtcGIzk6GcWqFoOjmyPUGjUc3RxRrGoxJEcn49y6cxCLKB1qvsDjnckLLwAdO1p/rlwZ+OMPYP58JjRyENsfEZH9YVKDiIiIiIiIKJOY8BhEhUXBrYwbVKqs4+mrVCq4lXHDvdB7iAmPUSjC/KVAH2+jEZAHbpZ/9RXwySfA2bNAUJAiYRUkBbr9ERHZKSY1iIiIiIiIiDIxxBuQZkiDzlX3yPU6Fx3SDGkwxBtyObL8qcAe7z/+AGrUAL7/Pmt5iRLAp58CTk6KhFXQFNj2R0Rkx5jUICIiIiIiIsrEyd0JWictTEmmR643JZugddLCyZ03nbNDgTveCQnAe+8BTZoAly9bf46OVjqqAqvAtT8ionzAbpMaP//8M3x9faHVatGgQQOEhYUpHRIRERERERHlA55+nvCq6gV9hB7ywNBAIgJ9hB7FqxWHp5+nQhHmLwXqeO/eDbz4IrBwYUZZxYrWRAcpokC1PyKifMIukxpXrlzB22+/jenTp+PWrVsoV64cBg4cqHRYRERERERElA+o1CoE9A2ASzEXRIdFw6g3wpJmgVFvRHRYNFyLucK/jz9UatV/74z+U4E43rGxQP/+QOvWwI0b1jJnZ2DuXODQIaBCBWXjK8AKRPsjIspntEoH8CzCwsIwdepUdOnSBQDw7rvvok2bNgpHRURERERElDvEIogJj4Eh3gAndyd4+nlm2w03S5oFV/ddReKdRBQqWQi+zX2h1mbP83A5GXd2K1WrFF6e9DKC1wTj9qnbMCWZoHPVwbuuNwL6BqBUrVLP/f8wp5oRvD4Y8Tfi4V7WHQG9A6Bx0GRD9PbXRtKP99k1Z63HO9kEnYsOpeuWzrbjnVNt+z/3u2UL8O67wJ07GWWvvAIsXw74+v7rvnOyjaQZ0vDn7D8R93ccPCp4IHBUILRO2XObKDUxFdve24a4q3Hw8PVA2wVt4VDIIVv2nRNx2/P1npPHOic/D4iInoddJjXatWuXZfmvv/6Cn5/fI7c1Go0wGo22Zb1en6OxERERERER5aTIM5EIXhuMqLAopBnSoHXSwquqV7bceDv/7XkcnnEY+pt6WEwWqHVquPm4IejjINToXiPPxp2TJP0/1T//PjA8zbP6Y9ofODLnCIzxRohFoFKrsHfMXjQa2QiNxzZ+rn3baxsBAFX6f2L9F9lzuHMs7v/c77x5wAcfZPyCmxswaxYwcCCg+vckU062ke3Dt+P0itMwG83WY6wCfv/8d9QeWBuvffXac+17Xet1+Hv337blG3/cwLm151ChVQX02dUnz8YN2N/1npPHOqevdSKi52H36dXU1FTMmjULQ4cOfeT6adOmwd3d3fby8fHJ5QiJiIiIiIiyR+SZSBz87CBun7oNZ09neFbyhLOnM26fuo2Dnx1E5JnIZ973+W/PY/eo3Yi9EguNowaOHo7QOGoQeyUWu0ftxvlvz+fJuAFrr4T7l+7j1olbuH/pPsTy/DciM8fs5O4Edx93OLk7ZUvMf0z7AwcnH4QhxgCVRgWNowYqjQqGGAMOTj6IP6b98dxxRxyPgCHWAKPeCEOsARHHI+ymjai1aji4OUCtVWd72zanmSEQmNPMzx135v2q1CponbVQqVVZ99utG1CkiPUX2rUDLlwABg16ooTGwckHkRKTAqhgfTpeBaTEpDx3G9k+fDtOLDoBs8EMqAGVVgWoAbPBjBOLTmD78O3PvO8Hb7Jn9vfuv7Gu9bpn3ndOxm1rfydvQ6PVwLGQIzRaDW6fzIHr3Sl7rvecPNaZ27bOVQfXEq7Queqy5VpPZ0414/TK0/jtk99weuVpmFPNz71PIio47LKnRmYTJkxAoUKFMHjw4EeuHzt2LEaOHGlb1uv1TGwQEREREZHdEYsgeG0wkqOTUaxqMaj+uSnq6OaIYlWLITosGufWnUPJgJJPPcyQJc2CwzMOw6g3Qq1TZ3maWK1Tw6g34s+Zf6L6W9WfeuiRnIwbyJleCekxx12LgyXNgvjr8ZA0gUqrgpOHE0xJpmeO2ZxqxpE5R2A2maHSqCBpAotYrMdFA5hNZhydexSBowKfemia9LgjT0ci6W5SlpuEGgcNXEu45uk2Eh0ajaToJJiSTLZ961x1cI16/riTo5NhMVkeWp9mSHumuNP3a4g1wGKxWGOGQC0WQKeFxWT5Z7+DoF6yBEhLA7p3/89kBpCpjfxz/iypmeJWWdc/axtJM6Th9IrTgMWaFFCrM+psUVkgaYIzK8+g1cxWTz2kU2pi6mNvsqf7e/ffSE1MferhkXIy7vT2FxUahZToFKQmpdran4OrA5Kjk5/7ereYLIAG1uvdlHG9W0yWZzqXOXms09t2amIqCnkXglplPdYOLg7QOmuReDvxma/1dDnZC4mICga77qmxZ88eLFmyBN988w10Ot0jt3F0dISbm1uWFxERERERkb2JCY9BVFgU3Mq42RID6VQqFdzKuOFe6D3EhMc89b6v7ruKuL/jIGaB2Wi92a5xsD5NbDaaIWZB7NVYXN13NU/FnVM9QGLCYxBxLALJUclIjkqG1kELRzdHaB20trKbR28+U8zB64NhiDNYFywAVIBKowJU/ywDSIlNQfD64GeK+9K2S0iISIA51QyVWgWVRgWVWgVzqhkJEQn4a+tfebKN/L3/b8TdiENqQirUOjV0zjqodWqkJqQi7kYcru67+sxxR4dFPzKhAVhvKkeFRj113Ff3XUVseCzSUtNgSbNApRLUxSkMlYVwNCUiLTUNMZdjrPvt0gXo0eOJEhrAP20k1mAdXumfIZag/ufff8pSYp6tjfw5+0/r0E0aZEkMAP8sazLmrHha297blq3bZZaTcae3P/0NPYwJ1qSd1llrTdYlGKG/oX/m9pd+vQvkkde7QJ7pes/JY31131Xob+rhVMTJltBIp1ap4VTECfE34p/pWgey9lxR69TQuViv9ezoqUZEBYfdJjWuXr2Knj17YvHixahWrZrS4RAREREREeUoQ7wBaYY06Fwf/UCXzkWHNEMaDPGGp953wu0EmFKsT5prHDVQq9VQqaxPQ2scNRAITCkmJNxOyDNxP9gDxNHNEWqN2tYDJP3p6mcZiiolNgXxN+NhSbPAycPJevNebb2J7+ThZO29cTMeKbEpT73vuOvWxABgvbmpUv0zh4TKmoAAADEL4q7HPfW+k6KSEHctDiLWXiVqjRpqtRpqjRoqrQoigrhrcUiKSnrqfedkG0m5n4LYq7GwpFmgc9FBo/3neGs10LnoYEmzIPZqLFLuP/3xjr8Rb70Z/i/MRjPib8Q/1X71EXqYkk2AAEU1cehtWYu2ll9RFPfRWrUbEMCUbII+4unn9UxPHgGwDrOUqY2k38URsyDu77hn2jcEDyUY06lU1nlMnmXfMVee7Kb/k26XWU7GnXw/GbFXYmExW6B10UKjtbZvjVYDrYsWFrO1/SXfT376uHPoeo+7+mTbP+l2mSXeSYTFZIHW8dE9XrSO1p5IiXcSn3rfmXuu6ArpoHXQQq1RQ+ugha6QztZzhUNREdF/scukRkpKCtq1a4eOHTuiQ4cOSExMRGJiYrZN4ERERERERJTXOLk7QeukhSnJ9Mj1pmQTtE5aOLk7PfW+xSLWG4bqfyZnzkQF65P+EDxTgiCn4s7JHiCGOAPMBrN1rotH7FvjqIHZYM7ocfEUbDcKVXjksU4vetwNxX9z7cA16w1UNR75hDXU1huo1w5ce+p952QbuRd6D+ZUszV59Kjj7aCBOdWMe6H3nnrfNw7fyNbt0iXeSQQsZjTEnxhiWojykjEUkEANlcoCEXmmG7+pSam2nx/ZRh6x3ZPyqOABqPDY+yciAqj+2e5pPel96Ge4X52TcUeFRsFsMkOtUz/yulHr1DCnmhEVGvXU+86p693D1yNbt8usUMlCUOvUSDOmPXJ9mjENap0ahUoWeup9B68PhjHeCI2T5pE9bjROGhjiDM/UC4mICha7TGrs2rULYWFhWL58OQoXLmx7Xb9+XenQiIiIiIiIcoSnnye8qnpBH6F/6MaeiEAfoUfxasXh6ef51Pv2quYFjYMGFpMFFkvWYXosFgssJgs0Dhp4VfPKM3HnZM8VJw8naB21SDOmWYeNyRwzBGlG69wdTh5Pn0Cq0LSC9QltM2CRB461WACz9YnuCk0rPPW+bTchBY+MO73ocTcr/01OthHbPAWWh29ai4htWK5nmXMl/Wn47NouXTHzXfTHSrSW3dDBmrCLgwc2aPviF00HiFgn9n6WG7/FqxW3/fzI4/GI7Z5U4KhAaBw11vb3iPMIM6B10iJwVOBT77ts47LZul1mORm3Sm1NLohFHnm8xWJNmDxL+yvftLy1bVkec71brG2vfNPyT7XftgvaZut2mfk294Wbj5t1vphHxGyINcC9rDt8m/s+9b7jb8RDLAK15tG3I9UaNcQiT91ziogKHrtManTs2NH6wfLAq3z58kqHRkRERERElCNUahUC+gbApZgLosOiYdQbYUmzwKg3IjosGq7FXOHfx/+Zbry5FHWBh68H1Bo10lLSYDaZYbFYYDaZkZaSBrVGDQ9fD7gUdckzcedkzxXnIs5wL+sOjdb61LDZZIaIwGyy9s7QaDVw93GHcxHnp963i5cLPMp7WHtNpAksZmuSwGK2TnYMNeBR3gMuXk9/rD3KeWQZ0kZEIPjn30xD4HiU83j6uHOwjRT2Lgydi842CbbFbO3lYDFbrMPQqKxJqsLehZ96356+T5Yse9LtYDIBU6ag6uc9UQa3AFhzRcdUDbBYOxRX4GudVF6lgs5FB7cyTz+vp1tZN2id/3lyXzJutqf3lgEArbMWbmWfft9aJy1qD6xta39m8z/n0Wy2tb9aA2o99WTbAFD0haLZul1mORl3Ye/C0DnroILqke1PBRV0zs/W/ly9XOFR3gMqleqR17tKpYJHeQ+4erk+1X4dCjmgQqt/T3xWaFXhqScJBwC1Vo2gj4PgUMgBibcTkZqcCovZgtTkVCTeToRDIQcEfhT4TJOEu5d1h0qtgsX8mDluzBao1Cq4l3V/6n0TUcFil0kNIiIiIiKigqhUrVJ4edLL8K7jjZSYFMSExyAlJgWl65ZGk0lNUKpWqWfar6efJ3xf8YVbWTc4ujnCkmZBWop1AmRHN0e4lXVDxeYVn6kXSE7FnZM9Vzz9PFG6QWm4eLnApagL0oxpMMYbkWZMg0tRF7h4uaBMwzLPvO8X2r4A9zLu0DhorDeszdYb1hoHDdzLuKNyu8rPtO+A3gFw8nCyDmujhvWGuPmfG+Fq63A3zkWcEdA74Jnizqk24tvc1za8kMZRY52MPNU6+bjGUQOogCK+RZ7pyfDAUYHQOGn+dRut8xM+4S8CtGoFTJoElcmaTItWFcMa7QDsUr2GVIsDYAHUOjU0DhoUrVT0mWL2be6LopWLQuOgybhrk97E1YDGQYNiVYo9074B4LWvXkO9ofWsx8ViTRLAYj0O9YbWw2tfvfZM+w3oHQDnYv+e6HPxcnmm9gfkXNzp7U+lsQ4tJ5Z/2p/F2v5UGtUzt7/0671wmcKPvN4Llyn8zNd7n119HpvYqNCqAvrs6vPU+0xXo3sNtJrdCkUqFoEpyYSku0kwJZng6eeJVrNboUb3Gs+034DeAXB0d4TZYH5kjxuzwQwnD6dnbiNEVHA8fQqbiIiIiIiIFFOqVimUDCiJmPAYGOINcHJ3gqef5zP10EiX3psi7nocEu8lQqPVWG+iqgBzmhmFixd+5l4gORV35pijw6LhVsYNOhedbXLm5+m5knnfyVHJ1ietNSqIWWCIM8DVK3v2nXQvCRaTtbeDRqexjlNfvNAz71vjoEGjkY1wcPJBWEwWqBysE3mLRSCpArVOjYYfNLTeLH+OuLO7jaQ/Gb571G4YE4xwcHeARquBOc2M1IRUOLk7PfOT4elP+J9YdMI6jJXqn5fAlux54if8VSqgTx/gwAFAo8G9tv3wzXE/JCda4Oyssz6NL9YJ0x0LOz5zzA8eD41OY4vZbDI/177TvfbVa2g1sxX+nP0n4v6Og0cFDwSOCnymng7pMrc/s8mccZz/+Vej0zxz+8vJuDMf79TEVDi6OUKtVcOSZrEtP+vxznLd3E209jL553pXaVUoXOL53lv77OqD1MRUbHtvG+KuxsHD1wNtF7R9ph4aD6rRvQaqv1UdV/ddReKdRBQqWQi+zX2fq91lbiOmRJN1bg2N2torxmB+rvcoIipYmNQgIiIiIiKyMyq16pmGcPk36b0pgtcGIyosCmkG67wR3tW84d/H/5l7gWSW3XE/GHPC7QRonbQoXbf0c8f80PFIsB6P0vVyYN//HOvi1Yo/974bj20MADgy5wiM8UZrckOtgnNRZzT8oKFtfXbGnR1tJP3J78MzDkN/U4+0JOtkxEUrFUXgR4HP/GQ4ANsT/KdXnIbZaLYlN7TOWtQaUOvfn/AXsSYz0vXrB5w9C/Tpg+J16qD5t+dtMadPNp0dMT94PCwmS7btO53WSYsm45s8934ye7D9iUWgUqvg5OH03O0vXU7E/eDxNiWZoNap4enn+dzHOyevd8A6FFWnNZ2eax+Po9aq4dfaL1v3+WAbMRvN2fYeRUQFB5MaREREREREBCBneoHktFK1SqFEjRLZ+jRx5n3n1PHIyX03HtsYgaMCEbw+GPE34uFe1h0BvQOy5ennnIy7RvcaqPZmtRyJ+6mf8E9MBMaOBVJSgBUrMspVKmD+/FyJOSeelM8NOdn+clJOHm97fG/NSfbaRogo72BSg4iIiIiIiGxyohdIToo8E/nQE9DhO8IR0DcgT/Yuya19axw0qD2gdo7sO6fiftS5vHf+Xradyyd+wn/PHmDQIOD6devyW28BrVsrEnNOPCmfG3Ky/eWknDze9vbemtPstY0QUd7ApAYRERERERHZpcgzkTj42UEkRydb59Rw1cGUZMLtU7cRdz0OL096OVtuLFPOyxPnMjYWGDUKWL06o8zZGbh9O+/GTEREVADl7T6LRERERERERI8gFkHw2mAkRyejWNVi1sl9NWo4ujmiWNViSI5Oxrl15yAWUTpU+g954lxu2QJUr541odGsGXD+PPD223kzZiIiogKKSQ0iIiIiIiKyOzHhMYgKi4JbGTeoVFnHpVepVHAr44Z7ofcQEx6jUIT0pBQ9l/fuAV27Ap06AZGR1rLChYGlS4G9e4GKFfNezERERAUch58iIiIiIiIiu2OINyDNkAadq+6R63UuOiTcToAh3pDLkdHTUuxcXrkCNGgA3L+fUfbaa8CSJYCPz7/+KtsfERGRcthTg4iIiIiI8q01a9ZApVI99FqzZg1CQkJQr149FClSBB999BFEOEyMPXFyd4LWSQtTkumR603JJmidtHByd8rlyOhpKXYufX2BgADrz56ewNdfA1u3/mdCA2D7IyIiUhKTGkRERERElG/16NEDsbGxttfNmzdRrFgxNGrUCO3bt0edOnVw8uRJhIaGYs2aNUqHS0/B088TXlW9oI/QP5SQEhHoI/QoXq04PP08FYqQnlSuncsHE5cqFbB8OdCrFxAaCvTsaS3LSzETERHRQ5jUICIiIiKifMvBwQEeHh6217p16/DGG28gLCwM8fHxmDNnDipWrIipU6di5cqVj92P0WiEXq/P8iJlqdQqBPQNgEsxF0SHRcOoN8KSZoFRb0R0WDRci7nCv48/VOonu0lNysmVc3n1KtCypXWejMx8fYH164ESJfJezERERPRITGoQEREREVGBYDAYMH/+fIwdOxbBwcFo2LAhXFxcAAD+/v4IDQ197O9OmzYN7u7utpfPEwxPQzmvVK1SeHnSy/Cu442UmBTEhMcgJSYFpeuWRpNJTVCqVimlQ6QnlGPn0mwG5s0DatQA9u0DBg0CEhPzdsxERET0rzhROBERERERFQjffPMNGjZsiPLly0Ov16NChQq2dSqVChqNBrGxsShSpMhDvzt27FiMHDnStqzX65nYyCNK1SqFkgElERMeA0O8AU7uTvD08+QT8nYo289laCgwYABw9GhGmQhw/TpQvXrejJmIiIj+E5MaRERERERUICxZsgSTJ08GAGi1Wjg6OmZZ7+TkhOTk5EcmNRwdHR/anvIOlVqFoi8UVToMygbZci5NJuDLL4EpU4DU1Izy994Dpk0DChV6vv0/gO2PiIgodzGpQURERERE+V54eDjCw8PRokULAICnpydCQkKybJOQkAAHBwclwiOi7HL6NNC/PxAcnFH2wgvAypXASy8pFxcRERFlG86pQURERERE+d6mTZvQrl076HQ6AEC9evVwNNOQNNeuXYPRaISnp6dSIRLR81q9GqhfPyOhodEAo0cDZ88yoUFERJSPMKlBRERERET53s6dO9GsWTPbcpMmTRAfH49169YBAKZPn44WLVpAo9EoFSIRPa/GjYH03lb+/sCxY8D06YCzs7JxERERUbbi8FNERERERJSvpaSk4NixY1i2bJmtTKvVYtmyZejRowc++ugjmM1mHDx4UMEoiei5+flZkxjx8dYeGhxOjoiIKF9iUoOIiIiIiPI1Z2dnGI3Gh8o7duyIy5cv4+TJkwgMDISXl5cC0RHRM9m715rA+PlnwNU1o3zECOViIiIiolzBpAYRERERERVYpUuXRunSpZUOg4ieVFwcMGoUsGqVdXniRGDOHEVDIiIiotzFpAYRERERERER5X0//wy8+y4QGZlRFhwMpKUBWt7eICIiKig4UTgRERERERER5V1RUUD37kDHjhkJjcKFgcWLgT17mNAgIiIqYPjJT0RERERERER5jwiwcaN1nozo6IzyV18Fli4FfHyUi42IiIgUw6QGEREREREREeUtIkC3bsCmTRllnp7AvHlAr16ASqVYaERERKQsDj9FRERERERERHmLSgXUqZOx3LkzEBoK9O7NhAYREVEBx54aRERERERERJT3jBwJHDoE9OsHvPGG0tEQERFRHsGkBhEREREREREpx2wGvvoKiI0FJk/OKNdqgV9+US4uIiIiypOY1CAiIiIiIiIiZYSFAQMGAEeOAGo10K4dUK+e0lERERFRHsY5NYiIiIiIiIgod5lMwNSpQM2a1oQGAFgswIEDSkZFREREdoA9NYiIiIiIiIgo95w5A/TvD5w9m1FWqRKwYgXQpIliYREREZF9YE8NIiIiIiIiIsp5BgMwfrx1eKn0hIZaDXz0ERAczIQGERERPRH21CAiIiIiIiKinHXjBtC6NXDxYkZZjRrAypWcQ4OIiIieCntqEBEREREREVHO8vYGXFysP+t0wKefAidPMqFBRERET41JDSIiIiIiIiLKWVqttVdGYCBw+jTwySeAg4PSUREREZEdYlKDiIiIiIiIiLJPfDwwZAhw6lTW8po1gUOHgBdfVCQsIiIiyh84pwYRERERERERZY9ffwXeeQe4fRs4ehQ4cSJrjwyVSrnYiIiIKF9gTw0iIiIiIiIiej5RUUCPHsDrr1sTGgDw99/AuXPKxkVERET5DpMaRERERERERPRsRICNG4Fq1YBvv80of/VV4MIFoG5d5WIjIiKifIlJDSIiIiIiIiJ6erduAR07At27A9HR1rIiRYB164Bt2wAfH0XDIyIiovyJc2oQERERERER0dPZsgXo1886KXi6N98EFiwASpZUKirKI8QiiAmPgSHeACd3J3j6eUKl5nwqRESUPZjUICIiIiIiIqKnU64ckJho/blECWDhQmtSgwq8yDORCF4bjKiwKKQZ0qB10sKrqhcC+gagVK1SSodHRET5AJMaRERERERERPR0atUCPv7YOgTV3LmAp6fSEVEeEHkmEgc/O4jk6GS4lXGDzlUHU5IJt0/dRtz1OLw86WUmNoiI6LlxTg0iIiIiIiIieryLF4HBgwGTKWv5F18Aa9cyoUEArENOBa8NRnJ0MopVLQZHN0eoNWo4ujmiWNViSI5Oxrl15yAWUTpUIiKyc0xqEBEREREREdHDTCZg2jSgZk1g+XJg5sys61WcI4EyxITHICosCm5l3KB6oG2oVCq4lXHDvdB7iAmPUShCIiLKL5jUICIiIiIiIqKszp4FGjQAxo0DjEZr2TffPNxbg+gfhngD0gxp0LnqHrle56JDmiENhnhDLkdGRET5DZMaRERERERERGRlNAITJgD16gFnzljL1Grgww+B48cB3aNvWBM5uTtB66SFKenRiS9TsglaJy2c3J1yOTIiIspvmNQgIiIiIiIiIuDoUesE4F98AaSlWctefBE4csQ69JSLi7LxUZ7m6ecJr6pe0EfoIZJ13gwRgT5Cj+LVisPTj3OwEBHR82FSg4iIiIiIiKggEwE++ggIDATCwqxlWi3wySfAqVNA/frKxkd2QaVWIaBvAFyKuSA6LBpGvRGWNAuMeiOiw6LhWswV/n38oVJzLhYiIno+WqUDICIiIiIiIiIFqVTWIabSn66vWxdYtQqoUUPZuMjulKpVCi9PehnBa4MRFRaFhNsJ0DppUbpuafj38UepWqWUDpGIiPIBJjWIiIiIiIiICrpPPwV27QJ69QLef9/aU4PoGZSqVQolA0oiJjwGhngDnNyd4OnnyR4aRESUbfgthYiIiIiIiKgg2boVuHULGDIko8zZ2TrUlEajXFyUb6jUKhR9oajSYRARUT7FpAYRERERERFRQRAdDfzvf8A33wCOjkDTpkDlyhnrmdAgIiIiO8CJwomIiIiIiIjyMxHgu++AatWsCQ0AMBqB1auVjYuIiIjoGbCnBhEREREREVF+dfs2MHQo8PPPGWVFigBz5wJ9+igXFxEREdEzYk8NIiIiIiIiovxGBFi1yto7I3NC4403gNBQoG9fQMWJm4mIiMj+sKcGERERERERUX5y9y7QuzewZ09GWfHiwMKFQOfOysVFRERElA3YU4OIiIiIiIgoPylcGPj774zl3r2tvTOY0CAiIqJ8gEkNIiIiIiIiovzExQVYsQIoWxbYtg1Ytw4oWlTpqIiIiIiyBZMaRERERERERPYqLQ2YMQO4fDlr+csvW8tee02ZuIiIiIhyCJMaRERERERERPYoOBho0AAYPRoYOBCwWLKud3BQJi4iIiKiHMSkBhEREREREZE9MRqBiROBunWB06etZYcOAUePKhsXERERUS7QKh0AERERERERET2hY8eA/v2tE3+nq14dWLUKqF9fubiIiIiIcgl7ahARERERERHldcnJwMiRQKNGGQkNrRaYNAk4dYoJDSIiIiow2FODiIiIiIiIKC87fBjo0we4ejWjrE4da+8Mf3/l4iIiIiJSAHtqEBEREREREeVlRmNGQsPREfjyS+v8GUxoEBERUQHEnhpEREREREREedkrrwCDBgFhYcDKlcALLygdEREREZFi2FODiIiIiIiIKK+4fx+YNg2wWLKWz58PHDzIhAYREREVeOypQURERERERKQ0EeD774H33gOiooCiRYHBgzPWOzsrFxsRERFRHsKeGkRERERERERKiowE3ngD6NrVmtAAgKlTAZNJ2biIiIiI8iAmNYiIiIiIiIiUIAKsXg1UqwZs2ZJR3qkTcOQIoNMpFhoRERFRXsXhp4iIiIiIiIhy27VrwJAhwO7dGWXFiwMLFgCdOwMqlWKhEREREeVl7KlBRERERERElJsWLgRefDFrQqNXLyA0FHjrLSY0iIiIiP4Fe2oQERERERER5abTp4GkJOvPpUsDS5cCbdsqGxMRERGRnWBPDSIiIiIiIqLcNHs24O1tHX7qwgUmNIiIiIieAntqEBEREREREeWUc+es82e8/npGmYeHNZnh4aFQUERERET2iz01iIiIiIiIiLKb0QhMmgTUqQP06QPcupV1PRMaRERERM/ErpMa9+/fR4UKFXDt2jWlQyEiIiIiIiKyOnbMmsyYMgVISwPi44Evv1Q6KiIiIqJ8wW6TGtHR0WjXrh0TGkRERERERJQ3JCcDo0YBgYHW4aUAQKsFJkwAZs5UNjYiIiKifMJukxrdunVDt27dlA6DiIiIiIiICDhwAPD3B+bMASwWa1nt2sDJk9YeG46OioZHRERElF/YbVJj2bJl+N///vef2xmNRuj1+iwvIiIiIiIiomyRkAC8+y7QrBlw5Yq1zNERmD7dOgxVQICy8RERERHlM3ab1PD19X2i7aZNmwZ3d3fby8fHJ4cjIyIiIiIiogLDZAJ++iljOSgICA4GRo+2Dj1FRERERNnKbpMaT2rs2LGIj4+3vW7evKl0SERERERERJRfeHoCixYBrq7AV18Bv/8OVK6sdFRERERE+Va+T2o4OjrCzc0ty4uIiIiIiAqWMWPGoH379rbl4cOHQ6VS2V5+fn4KRkd2ZfNm4PbtrGVvvAFcvQq89x6gzvd/ZhMREREpin1hiYiIiIgoXwsJCcGiRYtw5swZW9mpU6ewbds2BAYGAgA0Go1S4ZG9iIy0Ji1+/BHo2NH6r0qVsb54ccVCIyIiIipI+AgJERERERHlWyKCIUOG4P3330fFihUBAGlpaQgJCUGTJk3g4eEBDw8PFC5cWOFIKc8SAdasAapVsyYyAGDLFuCPP5SMioiIiKjAYlKDiIiIiIjyreXLl+Ps2bOoUKECtm7dCpPJhHPnzkFEULNmTTg7O6NNmza4cePGv+7HaDRCr9dneVEBcOMG8OqrwNtvA3Fx1jIvL2DjRqBxY0VDIyIiIiqo7D6pISIoX7680mEQEREREVEek5iYiAkTJqBSpUqIiIjAnDlz0KRJE4SFhaF69er49ttvERoaCp1OhyFDhvzrvqZNmwZ3d3fby8fHJ5dqQYqwWKyTf1evDuzalVHesycQGgp07Zp16CkiIiIiyjWcU4OIiIiIiPKlH3/8EUlJSdi/fz88PT0xduxY1KhRA0lJSTh69KhtuwULFsDX1xd6vR5ubm6P3NfYsWMxcuRI27Jer2diI7+6fBkYMCDr8FKlSwNLlgDt2ikXFxEREREBYFKDiIiIiIjyqYiICDRo0ACenp4AAK1WC39/f/z9999ZtvPw8IDFYkFkZORjkxqOjo5wdHTM8ZgpDzh7NmtCY/BgYMYMwN1dsZCIiIiIKIPdDz9FRERERET0KD4+PkhJSclSdv36dSxduhSbNm2ylZ04cQJqtZo9L8iqc2egUyegQgVg3z5g6VImNIiIiIjyEPbUICIiIiKifKlt27YYPnw4lixZgnbt2uHHH3/E2bNnMW7cOIwfPx4lS5ZEWloahg8fjn79+sHFxUXpkCm3paYCP/1knSMjnUoFLF8OODkBrq7KxUZEREREj8SkBhERERER5Uuenp7YuXMnRo0ahZEjR6JkyZLYuHEjOnbsiJSUFHTo0AGFCxdGp06dMHXqVKXDpdx24gTQvz8QEgJotcCbb2asK1pUubiIiIiI6F8xqUFERERERPlWw4YNcfjw4YfKp02bhmnTpikQESkuORn45BNgzhzAYrGWvf8+0L494OCgaGhERERE9N+Y1CAiIiIiIqKC4eBBYOBAIDw8o6xWLWDVKiY0iIiIiOwEJwonIiIiIiKi/E2vB959F2jaNCOh4egITJ0KHDsG1KypZHRERERE9BTYU4OIiIiIiIjyrx07gCFDgJs3M8oCA4GVK4EqVZSLi4iIiIieCXtqEBERERERUf61dGlGQsPFBZg/H/j9dyY0iIiIiOwUkxpERERERESUfy1aBLi7A82bAyEhwIgRgEajdFRERERE9Iw4/BQRERERERHlD3fuANeuAQ0bZpR5ewMnTgB+foBKpVhoRERERJQ92FODiIiIiIiI7JsIsG4dUK0a8MYbQFxc1vWVKjGhQURERJRPMKlBRERERERE9uvGDeC114C+fYHYWCAyEpg0SemoiIiIiCiHMKlBRERERERE9sdiARYvBqpXB3buzCjv3h2YOFG5uIiIiIgoR3FODSIiIiIiIrIvly8DgwYBBw9mlHl7A0uWAO3bKxcXEREREeU49tQgIiIiIiIi+2A2A7NmAf7+WRMaAwcCFy4woUFERERUALCnBhEREREREdmHu3eBKVMAg8G6XKECsHw50Ly5snERERERUa5hTw0iIiIiIiKyD97ewMyZgEoF/O9/wPnzTGgQERERFTDsqUFERERERER508mTgJ8f4OGRUTZoENCgARAQoFhYRERERKQc9tQgIiIiIiKivCUlBfj4Y2vy4qOPsq5TqZjQICIiIirAmNQgIiIiIiKivOP3361Ji5kzAYsFWLECOHRI6aiIiIiIKI9gUoOIiIiIiIiUl5AADBsGvPwycPmytczBAfjiC2uPDSIiIiIicE4NIiIiIiIiUtquXcDgwcCNGxlljRoBK1cCVasqFxcRERER5TnsqUFERERERETKiIkB+vUD2rTJSGi4uADz5gF//MGEBhERERE9hD01iIiIiIiISBk//gisXZux3Lw5sGwZ4OurXExERERElKexpwYREREREREpo39/6xwabm7A8uXAnj1MaBARERHRv2JPDSIiIiIiIsp5IsCxY0DDhhllarW1p4ZWC5QurVxsRERERGQ32FODiIiIiIiIctbNm0DbttbJvw8cyLquXDkmNIiIiIjoiTGpQURERERERDnDYgGWLAGqVwd27LCWDRgAGI3KxkVEREREdovDTxEREREREVH2Cw8HBg4EDh7MKCtVCpg7F3B0VC4uIiIiIrJr7KlBRERERERE2cdsBmbPBvz9syY0BgwAQkOB119XLjYiIiIisnvsqUFERERERETZ48IFoH9/4PjxjLLy5YHly4EWLRQLi4iIiIjyD/bUICIiIiIiouzxv/9lJDRUKmDECOD8eSY0iIiIiCjbMKlBRERERERE2WPRIut8GZUrA3/8AcyfDxQqpHRURERERJSPcPgpIiIiIiIienopKUBEBFCpUkbZCy8Au3YBDRoATk7KxUZERERE+RZ7ahAREREREdHT+eMPoGZNoG1ba3Ijs5dfZkKDiIiIiHIMkxpERERERET0ZBISgPfeA5o0AS5dAi5fBj79VOmoiIiIiKgA4fBTRERERERE9N927wYGDQJu3Mgoa9QI6NdPsZCIiIiIqOBhTw0iIiIiIiJ6vNhY4O23gdatMxIaLi7A3LnWYaiqVlU2PiIiIiIqUNhTg4iIiIiIiB7tp5+AoUOBO3cyyl55BVi+HPD1VS4uIiIiIiqwmNQgIiIiIiKih926BXTvDhiN1mU3N2DWLGDgQEClUjY2IiIiIiqwOPwUERERERERPax0aeCTT6w/t2sHXLhgnVODCQ0iIiIiUhB7ahAREREREREQEQEUKwY4OWWUffghUL060L49kxlERERElCewpwYREREREVFBZrEAS5cC1aoBkydnXafTAa+/zoQGEREREeUZTGoQEREREREVVFeuAM2bA++8AyQkADNnAqdPKx0VEREREdFjMalBRERERERU0JjNwNy5QI0awIEDGeV9+gAVKigWFhERERHRf+GcGkRERERERAVJaCgwYABw9GhGWblywPLlQMuWysVFRERERPQE2FODiIiIiIioIDCZgM8/B2rVykhoqFTA8OFASAgTGkRERERkF9hTg4iIiIiIqCBYsQKYODFjuXJla9lLLykXExERERHRU2JPDSIiIiIiooJg4EAgIADQaIAxY4CzZ5nQICIiIiK7w54aRERERERE+VFEBFCmTMayTgesW2cdhqpOHeXiIiIiIiJ6DuypQURERERElJ8kJlrnyfD1tfbGyMzfnwkNIiIiIrJrTGoQERERERHlF3v2AC++CCxYYO2R0b+/9V8iIiIionyCSQ0iIiIiIiJ7FxtrTWC0agVcv24tc3YGevcG1Pyzj4iIiIjyD86pQUREREREZM+2bAGGDgUiIzPKmjUDli8HKlZULCwiIiIiopzAR3aIiIiIiIjs0b17QNeuQKdOGQmNwoWBpUuBvXuZ0CAiIiKifIk9NYiIiIiIiOxRt27Ab79lLL/2GrBkCeDjo1xMREREREQ5jD01iIiIiIiI7NGXX1rny/D0BNavB7ZuZUKDiIiIiPI99tQgIiIiIiKyR/XqWZMZzZsDJUooHQ0RERERUa5gUoOIiIiIiMhe9eihdARERERERLmKw08REREREREREREREZFdYFKDiIiIiIiIiIiIiIjsApMaRERERERERERERERkF5jUICIiIiIiIiIiIiIiu8CkBhERERERERERERER2QUmNYiIiIiIiIiIiIiIyC4wqUFERERERERERERERHaBSQ0iIiIiIiIiIiIiIrILTGoQEREREREREREREZFdYFKDiIiIiIjyvTFjxqB9+/a25ZCQENSrVw9FihTBRx99BBFRMDoiIiIiInpSTGoQEREREVG+FhISgkWLFmHevHkAAKPRiPbt26NOnTo4efIkQkNDsWbNGkVjJCIiIiKiJ8OkBhERERER5VsigiFDhuD9999HxYoVAQA7duxAfHw85syZg4oVK2Lq1KlYuXLlv+7HaDRCr9dneRERERERUe5jUoOIiIiIiPKt5cuX4+zZs6hQoQK2bt0Kk8mE4OBgNGzYEC4uLgAAf39/hIaG/ut+pk2bBnd3d9vLx8cnN8InIiIiIqIHMKlBRERERET5UmJiIiZMmIBKlSohIiICc+bMQZMmTaDX61GhQgXbdiqVChqNBrGxsY/d19ixYxEfH2973bx5MzeqQERERERED2BSg4iIiIiI8qUff/wRSUlJ2L9/PyZOnIjdu3cjLi4Oq1atgqOjY5ZtnZyckJyc/Nh9OTo6ws3NLcuLiIiIiIhyH5MaRERERESUL0VERKBBgwbw9PQEAGi1Wvj7+8NgMCAqKirLtgkJCXBwcFAiTCIiIiIiegp2m9QICQlBvXr1UKRIEXz00UcQEaVDIiIiIiKiPMTHxwcpKSlZyq5fv47Zs2fj6NGjtrJr167BaDTakh9ERERERJR32WVSw2g0on379qhTpw5OnjyJ0NBQrFmzRumwiIiIiIgoD2nbti3CwsKwZMkSRERE4P/+7/9w9uxZtGrVCvHx8Vi3bh0AYPr06WjRogU0Go3CERMRERER0X/RKh3As9ixYwfi4+MxZ84cuLi4YOrUqRg2bBjefvvth7Y1Go0wGo225fj4eACAXq/PtXgfJVn/+PF67YkeT3cc80O9WecnUxDrzTrbr4JYb9b5yRTEerPO9ulZ2ne2x/DP9+u81IPa09MTO3fuxKhRozBy5EiULFkSGzduhJ+fH5YtW4YePXrgo48+gtlsxsGDB59q3+n1VPrvCiIiIiKi/OJJ/6ZQSV76q+MJTZ48GceOHcP27dsBWCtZtGhRxMTEPLTtp59+ismTJ+d2iEREREREBdLNmzdRpkwZpcN4Irdu3cLJkycRGBgILy+vp/rdiIgI+Pj45FBkREREREQF13/9TWGXSY1Ro0bBYDBg4cKFtjIvLy9cunQJRYoUybLtgz01LBYLYmJiULRoUahUqlyLObfp9Xr4+Pjg5s2bcHNzUzqcXFEQ6wwUzHoXxDoDBbPerHPBqDNQMOtdEOsMFMx6F5Q6iwgSEhLg7e0NtdouR7l9KhaLBbdv30bhwoX/9e+KgnL+8zuex/yD5zJ/4HnMH3ge8w+ey/whL5zHJ/2bwi6Hn9JqtXB0dMxS5uTkhOTk5IeSGo6Ojg9t6+HhkdMh5hlubm4F7s2kINYZKJj1Loh1BgpmvVnngqMg1rsg1hkomPUuCHV2d3dXOoRco1arn6pHSkE4/wUBz2P+wXOZP/A85g88j/kHz2X+oPR5fJK/KezyESpPT09ERUVlKUtISICDg4NCERERERERERERERERUU6zy6RGvXr1cPToUdvytWvXYDQa4enpqWBURERERERERERERESUk+wyqdGkSRPEx8dj3bp1AIDp06ejRYsW0Gg0CkeWdzg6OuKTTz55aOit/Kwg1hkomPUuiHUGCma9WeeCoyDWuyDWGSiY9S6IdaYMPP/5A89j/sFzmT/wPOYPPI/5B89l/mBP59EuJwoHgC1btqBHjx4oXLgwzGYzDh48iOrVqysdFhERERERERERERER5RC7TWoAwK1bt3Dy5EkEBgbCy8tL6XCIiIiIiIiIiIiIiCgH2XVSg4iIiIiIiIiIiIiICg67nFODiIiIiIiIiIiIiIgKHiY1iIiIiIiIiIiIiIjILjCpQURERERElI3GjBmD9u3b25ZDQkJQr149FClSBB999BE4ArD9ePBcDh8+HCqVyvby8/NTMDr6N2vWrMlyrtJfa9as4TVpZ/7tXPKatC/r169H2bJlUahQIbRo0QLXrl0DwM9Je/O488jr0b6sXr0aL774Ijw8PNC9e3dER0cDsJ/rkUkNIqJ8Iq9+0OQEo9GIyMhIpcPIEywWS5blgtQOgKz1NZvNCkaSMzLX7/LlyzCZTApGQ7nhwWua7E9ISAgWLVqEefPmAbB+ZrVv3x516tTByZMnERoaijVr1igaIz2ZB88lAJw6dQrbtm1DbGwsYmNjcebMGeUCpH/Vo0cP23mKjY3FzZs3UaxYMTRq1IjXpJ153Lls0qQJr0k7cuXKFYwfPx5btmxBaGgoypUrh379+vFz0s487jwC/Iy0J3v37sWIESMwd+5cBAcHQ6/Xo1OnTnZ1PTKpQfn+hhA9mbS0NERFRSkdRo7I3Mbv37+vYCQ5R0SgUqkAAKGhoUhOTlY4opyTkJCAVq1aYebMmTAajUqHoyiLxQK1Wo3k5GRs374dAGztoCDI3O4/++wzjBs3TuGIslfm+k2YMAFdu3bFzp07831ioyB+LzEajVi3bh0AQK1Ww2w2F7gEZX4hIhgyZAjef/99VKxYEQCwY8cOxMfHY86cOahYsSKmTp2KlStXKhwp/ZdHncu0tDSEhISgSZMm8PDwgIeHBwoXLqxwpPQ4Dg4OtvPk4eGBdevW4Y033kBYWBivSTvzuHNZtmxZXpN25MyZM2jYsCFq166NsmXL4u2338alS5f4OWlnHnce+RlpX9atW4eBAweiZcuWKFeuHGbOnIlDhw5h69atdnM9MqlBthsmiYmJ0Gg0sFgsOH/+vMJRKcNisRTImwhmsxnjx4/H4sWLERcXp3Q42SrzTcFPPvkEAwYMQHh4uMJRZa/MdRw/fjw6deqEX3/9FSkpKQpHlv0SEhIQGBiIpKQk3Lp1C4mJiQAK5pPNycnJUKvVSEhIQJcuXfDpp58iJiamwLyHZW73Y8eOxVdffYXZs2fjww8/VDiy7PFg/TZu3Ag/Pz+sWbMGu3fvzteJjfR6x8TEQKPRwGw24+TJk/m6be/btw8rV67E0qVLAQBxcXEFKkGZnyxfvhxnz55FhQoVsHXrVphMJgQHB6Nhw4ZwcXEBAPj7+yM0NFThSOm/POpcnjt3DiKCmjVrwtnZGW3atMGNGzeUDpWegMFgwPz58zF27Fhek3Yu87nkNWlfqlWrhv379+PMmTOIj4/HwoUL0bJlS16TduZx55HXo32Jjo5G2bJlbcsajQaAtZeqvVyPTGoQAGDixIkIDAxEfHw8KlSogF9++UXpkHJccnIyFixYgFu3btnKJk2ahEOHDikYlTI0Gg18fX0RERGBn376Ce+//z4OHjyodFjP7cGbgqtXr0Z4eDg+/fRT/P333wpHlz0erOPmzZvx0ksvYf369di2bVu+SmwYDAY0aNAAr7zyCk6ePAmtVouePXsCyHiyuaCIiYnBzJkz8ddff6FNmzZwdnaG2WzGvXv3HroRmh9vBD+q3UdFRSE8PByLFy+2+8TGo+oXGhqKTZs2oXjx4li6dCn27t2LtLQ0hSPNOV9++SWqV6+OyMhIVKhQAVu2bMnXN/nr16+Pjz76CEeOHEGNGjUwceJEAAWnp0p+kZiYiAkTJqBSpUqIiIjAnDlz0KRJE+j1elSoUMG2nUqlgkajQWxsrILR0r953LkMCwtD9erV8e233yI0NBQ6nQ5DhgxROlx6At988w0aNmyI8uXL85q0c5nPJa9J+1KtWjV07twZtWvXhoeHB44dO4ZZs2bxmrQzjzuPvB7tS82aNfHLL7/Y7hesXr0a9evXt6/rUYj+4e/vLx4eHtKiRQtbmcViUTCinHXs2DFxd3eXWbNmicFgkD59+kirVq2UDivXZT7HW7ZskYoVK0rlypXl7t27Ckb1/DLXa8yYMVKpUiUREUlNTZWGDRtK9+7dJTw8XKnwssWj6picnCwiIh9//LG0atVKfvjhB0lJSVEqxGyVlJQkNWvWlPfff19EROLi4qR79+6ybt26LNvl5/etzIKCgkSlUsnkyZNFRGTQoEGybds2ERExmUwye/ZsMRqNSoaY44YNGyZVqlSRe/fu2cpOnTolKpVKPvnkE+UCyyYDBgyQF154IUv9kpKSxNXVVVq0aCFbt26V1NRUBSPMWc2aNZPChQtL8+bNbWX58fo2m80iIpKWlibNmjUTHx8f+b//+7+H1lPet3btWnFxcZH79++LiPW9uEqVKuLh4SEffPBBlm3LlCkjERERSoRJT+Bx53Lp0qVZtrt27Zqo1WqJj49XIkx6CvXq1ZPt27eLiPV7Mq9J+5X5XD6I12TeduTIEfH29pZjx45JQkKCjBs3TurWrctr0s487jw++D2d12Pedv/+fQkMDJRatWpJo0aNBICsW7fOrq5H9tQgm9TUVFgsFqSmptqe/syvT/yKCOrXr4+9e/di48aNaNy4MS5evIhdu3Y9dvv8SqVS2Xqr/PLLL/Dw8EDTpk2xfft23Lx5U+Honl162x08eDB++uknHD58GACg0+mwefNmbNy4EZMnT7brHhvpdRwwYAA2b96Mw4cPw9nZGYD1KecjR45gzpw52Lp1q9322Mg8xI6Liwv27t2Ls2fPYvjw4TCZTAgICMD69evRqlUrjBs3DqdOnYJKpSoQw1ENHToULi4uuH//Pnbt2oUzZ85g3bp1WLp0KYKCgrBs2TLbNZyf3sOSk5OxceNGHDlyBNu2bcP+/fvh5eUFwDovwciRI9G6dWvMmjULI0eOVDjap5eSkoJ+/frhwoULuHTpEn777Tdb/VJTU9GqVSt06tQJlSpVwrJly7B79+5822MjMjISjo6OuHv3LuLj4wFkvO+lX+P54Vr/888/ce/ePUyZMgUWiwVLlizB2bNnMXPmTADWnmj5oZ4FQUREBBo0aABPT08AgFarhb+/PwwGw0PzliUkJMDBwUGJMOkJPO5cPvi90cPDAxaLBZGRkUqESU8oPDwc4eHhaNGiBQDA09OT16SdevBcPojXZN723XffoVu3bqhfvz4KFSqEzz//HFevXuU1aWcedx6Dg4OzbMfrMW/z9PTE4cOHsWnTJvj7+6NKlSro0aOHXV2PTGoUYJlvcn311VeoXr064uPjodfr0axZM9tN0JSUFKxfvx5msznfDP2QfnOgbt26KF26NE6ePInY2FhMmjQJGzZswOnTpxEcHIzNmzfDZDLlm3pnln4M9uzZg3bt2uH111+HwWDAyZMn0a1bNxw/fhxbt27F7du3FY706SUnJ+Ozzz7D1atXcfToUezbty/LTc9u3brhzTffRHh4OMaPH2+XiQ2DwYA33ngDZ8+eRVRU1EM3PoOCgtC5c2c0adIEK1assMuhqMxmM9asWYPBgwdjypQp+PDDDzFp0iQMHjwYx48fx8yZMzFgwAB888036NGjBwwGA1566SUcP34canX+/Hi7desWUlNTAQBt27aFj48Pzp07h+DgYJw6dQq7du2CVqtFcnIyGjVqZPsykp/ew/bt24cRI0Zg48aN+Pvvv1GqVCkA1ve0Jk2aoFy5ctixYweOHz+OZcuWYcyYMXaV1NHpdNi7dy/+97//Yc+ePfD29gZgrV9QUBD8/Pywfv16LFy4EGXLlsWiRYuwZ8+efDHHRubztG7dOvj7+yMqKgolSpRAUFAQ7t+/D8Ca7Fy5ciWSkpLs/lrftWsX2rdvj/Lly8NiseDAgQOoU6cO3nzzTVy9ehULFiwAwMSGvfDx8Xnos/b69euYPXs2jh49aiu7du0ajEaj7YY55T2PO5dLly7Fpk2bbGUnTpyAWq2Gj49PbodIT2HTpk1o164ddDodAKBevXq8Ju3Ug+dy5MiRvCbtSFpaGu7evWtbTkhIQFJSErRaLa9JO/K48/jBBx/werRD3t7e+PHHHzFt2jRoNBr7+oxUsJcIKchkMj12ncViEX9/f2nSpIkEBwfL+PHj5cUXX7T74YgMBoOsWrXK1mXKYrHIwIEDpUGDBjJy5EhRqVTi4eEhLVu2lEqVKtmGYTpx4oTCkWevzMPRbN26VapWrSrvvvuufPLJJ3LgwAFZt26d/PTTT/L222/Lxx9/LIsWLZLIyEgFI356oaGhotFopGfPnlnKzWaz1KtXT/r06SMi1u52QUFB0rNnT7sciqpmzZpSq1YtiY2NtZWZzWapXbu29OvXz1Y2YcIEeeWVV+SHH36wDU9lD0wmk4wePVqcnZ1l9OjRMnLkSGnZsqWsWbNG9Hq9NG7cWIYMGZJlaJZXX31Vpk6dmi+HqLl06ZK0b99ebt++LefPn5eVK1dKjx495M8//5S0tDRp27athIeHy5o1a6RKlSoybNgwGT16tJw6dUrp0LOVyWSSH374QQICAmTZsmUiYm33AQEB0qtXryzbLlmyRFQqlXz44YdKhPpcatWqJU2bNrUtt2/fXt56660s2+zcuVNUKpW0aNFCtm3bJmlpabkdZrb5t+8lItahqF588UU5dOiQjBkzRl544QW7+2wSsQ4fduDAATGbzXLq1CmpUqWKbN68WZYvXy69evWSb7/9Vv73v/9JUFCQvP/++9KlSxeZN2+e7ffz43tbfnL//n1xd3eXxYsXy82bN2X+/Pni6Ogoly9fFi8vL1m7dq2IiAwZMkTatWuncLT0bx53LidPnix+fn5y8OBB2bdvn1SpUkX69++vdLj0Hxo3biyrVq2yLZtMJl6TdurBc7l27Vpek3bk22+/FWdnZ5kzZ45s2LBBmjVrJmXLlpXU1FRek3bkcedxzZo1vB7t0PTp06Vx48a2ZXv6jGRSowBKvwFoNpuld+/e0qVLF1m0aJFYLJYsY+83btxYmjdvLr6+vvniptjGjRslMDBQ5syZI/Hx8bJs2TL57bffRERkx44dUqhQIalTp44sXrxYLly4IPHx8VnGMc8PLBaLNG3a1HZT/8MPP5SdO3dKWlqaTJ48Wb7++mv56aef5OOPPxZfX18ZMmSIDBo0SBYtWiS3bt1SOPqnExoaKuXLl5fhw4fbyoKCgqRbt25Ztvv+++9FpVJJly5d7DKx0aJFC/H397fd6OrcubN06dIlyzYHDhwQlUolQUFB8sMPP9jVPAvBwcESFBQkJpNJbt68mWWdXq+XNm3ayKBBgyQ1NVXCwsLEyclJfv31V4WizXktWrSQYcOGyYkTJ6Rz587y5ZdfyunTp+Xu3bvy5ptvytChQ6VatWpy9+5dSUlJkV69esnQoUPl0KFDSoeeLX777TcJCQkREet7eoMGDWT16tWSmJgon3/+eZZtd+/eLaVLl5Zp06aJj4+PfPvtt0qE/FR+/vlnmT17tm25evXq0qZNGxGxjl2b2Z49e6RkyZKyYcMGGTdunHz11VdiMBhyNd7skvl7SdeuXaVt27Yye/ZsSUtLy/K9pEOHDtKyZUupWrWq3X4vWbJkiRQtWlRCQ0MlJCRENmzYICIie/fulZEjR8ru3bslLCxMpkyZIosXL5bdu3dL9+7dZdasWQpHTk/qyJEjEhgYKM7OzlKhQgX56aefRETkp59+EmdnZylevLgULVrU9l5GedfjzuWYMWPEw8NDfHx8ZMSIEZKYmKhsoPSvkpOTxcHBQcLCwrKU85q0P487l7wm7YfFYpFPP/1UypYtKzqdTmrVqiUnT54UEV6T9uTfziOvR/sSGxsrnp6ecvz48Szl9nI9MqlRQFksFqldu7a0aNFCZsyYIdHR0SLy8GSUkZGREhMTo0SIOWLZsmXyxhtviL+/vwQGBtrKTSaT9OvXT6ZOnSo1a9aUiRMn2iYGzG+OHz8utWrVktmzZ8vQoUMlODhY1q5dK76+vlKxYkU5ePCgREVF2Z7o//nnn2Xw4MGyfv16hSN/Mlu3bpWtW7eKiEhISIhUrlxZPvroIxER282jdLt37xZvb29ZunSptG3bVpYuXWoXTzpv3bpVxo8fb1tu3Lix1K1bV0REzp8/n2Xb9BufP/zwg3z55Zcyd+5cu5k43GKxyF9//SXe3t6yePFief3116VTp04iIvLll1/K9OnTJTExUV5++WV577335PLly/Ljjz/afje/yNwmDxw4IBMnTpS4uDipUqWKnDt3TkREevbsKe7u7lKrVi35/fffRcR6rRcvXlxatmwpX3/9tSKxZ6fY2Fj58ssvpUmTJrYedCtWrJDGjRvLzp07s2y7f/9+8fLysiUybty4YRe9DY8dOyaurq4yduxYERFJSUmRSpUqSe/evbNsl16/jRs3iohIamqq3U/AZ7FYpF69etK6dWtZuHCh7bvHgxOhR0dHS1xcnBIhZpuZM2dKUFCQNG/eXGbNmiWbNm2SgIAAKVu2rNStW1dmzpwpR44csfUs/e2336Rt27Zy8eJFhSOn5xURESFbtmzJdw/NENkrXpNEeQuvSaK8wx6uRyY1Cqhz587JSy+9JAkJCSIiMnv2bBk+fLgMHTo0X97Mz3xTpG7duuLi4iIzZsyQ77//XiZMmCDXr1+XwMBAWb9+vYSEhEjNmjVl2rRpcvv2bQWjzn7pN3pv3bolxYoVE29vbxERuXz5sixZskR69+4tKpVKGjVqJK+++qrthmmfPn3klVdeyfNP+KekpMiGDRvEx8dHvvnmGxGxPmVXvXr1LE8/izx80zMuLs5u2v7FixfFy8srS1fO+vXry2uvvZZlu/3790vx4sVtNz5FrL0b7EHmpMTLL78su3fvlt9++022bNkigwYNEpVKZUtwxMbGyksvvSSjRo2SpKQkpULOETt27JBVq1bZ6vX333/LCy+8IG+88YaUKlXK1q4///xz2bBhg7z11lsyc+ZM2bBhg1SqVEn27dtnt0/vi4gkJiZm6WERFhYmY8aMkVdeeUVOnz4tItYEV+/evW0Jvd27d0vRokXl+++/FxHJ04lKvV4v7777bpbr8vDhw+Lq6ioTJ04UEZHr169Lw4YNbU/q7927VxwdHe2ifk/jr7/+kpdeesmWfJo6daoMHDhQ+vbtK9euXVM4uuxXtWpVUavVcvbsWRGxJiGvXLkiVapUkRYtWkizZs3kyy+/FLPZLFu3bpUiRYo81FuNiIiIiIiooLLv2RXpickDk6S6uLjg8OHD6N+/Pzp06IBly5bBw8MDv//+OzZu3KhQlNnnwfrqdDqYzWbMnDkT3t7eeP311zF27FgsW7YMq1atgoggMDAQv/zyC6pXr4758+djwYIFWLlyJcxms0K1yD7pxyN9suBZs2ahZMmScHR0xODBg7FkyRIUL14cderUQb169bB9+3b06NEDbm5uAICKFSti9uzZcHBwUKwOj5OQkIBp06YBAJycnNChQwdMmjQJ48ePx6ZNm9CwYUNMmzYNO3fuxK+//goA2L17Nzp27IhFixahW7duMJvNcHd3z5sTH8Fax169euHWrVsAgMqVK+OPP/7Ar7/+iiFDhgCw1slgMOCTTz4BAOzfvx9t2rTBggUL0LVrV1s7Lly4sDKVeAIigsOHDyMyMtLWVg0GA+Lj46HX69G0aVOcPn0aO3bswJYtW3D79m1cvHgRHh4e2LZtG7Zt24bRo0crXIvss3LlSrz22mtYs2YNdu3ahcTERJQvXx6lSpWCyWTC1KlTERoail27diEhIQE9evTAihUrcPbsWYwePRoffPABXnnlFdt1++D7Yl6XkJCARo0aYdCgQfj8888BAFWqVEH//v3RqFEjjBw5EufPn0ffvn0RHR2N4OBgJCUloXPnzliwYAE6d+4MEcmzE0knJCQgKCgIS5YswUsvvWS7RgMDA7F3717MmjULU6ZMQdmyZfH222/jzJkzuHv3LlauXIm1a9fa6qfRaBSuybN5sD26ubnh+PHj6NGjBzp06ID169fDz88PISEh+P777xWKMvuMHDkS27ZtAwD8+OOPqFmzJvr06YMGDRrY2sHXX3+NypUrY+zYsdi6dSuGDBkCtVqNatWq4dSpUyhTpozCtSAiIiIiIsojFEunUK55cPLN9Kft9+zZIx988IHMnDnTtq5t27YyZ86cXI0vu2V+wvvdd9+VcePG2ZaDg4NFROTu3btSqlQpad26tXzyySdy9+5dWb16tTRp0sR2fA4dOmSXcyxklvncZx5a7Mcff5TExEQZN26ceHh4SOXKlWXPnj1y9epVeemllx6qd159Eliv10uNGjVEpVJJ165dbeUpKSmycuVKKVeunG04ohEjRsj48eMlMTFRatWqZevJkdeHKdLr9eLv7y/Ozs5Srly5LMPBXbp0SYoWLSrDhg0TEZFvvvlG3nzzTYmMjJRBgwbJpk2bRCTv1zE9voiICGndurV88MEHWdru//3f/0lkZKRMmTJFihcvLleuXBERkX79+smVK1ds2+r1erl69WruVyAHrFy5UsqUKSMXL16UMWPGSN++fWXz5s0iIrJ9+3a5ceOGiIhUq1ZNOnbsKBs3bpT58+fLwYMHJTo6Wpo2bSqbN29+aEhBe6HX6+XFF1+UYcOGyfHjx6VJkyYydepU2/pLly7JxIkTpVGjRhIaGiqrV6+WcuXKidlslr/++ktErO0qr7b99Pp9/PHHIiLSunVrCQgIyBLv0aNHRa1Wy/jx4229644dO2b7jMrL9fsvD34vSe+JdPbsWRk/frwsWLDAtq5Dhw4yefLkXI0vu82cOVOqVatm67qdPlTYvXv3pFKlSuLl5SX79u2TlJQUmTt3rowYMUJEHh4SlIiIiIiIiKy0SidVKGdZLBZotVpYLBYMHjwYRqMRsbGxGDlyJFq0aIFXXnkFp06dwsqVK3Ht2jUcPnwYs2bNUjrs55L+hPfEiRNx6tQp7Ny5E/fu3YPFYoG/vz8A6xP9Pj4+iIuLw/Xr17F69Wp069YNrVq1goODAywWC4KCgpSsxnMzmUwYMmQIKlasiPHjx0OtVsNgMMDJyQmdOnUCADRt2hRz585FbGwsfv/9d7Ro0QIpKSk4c+YMKlasaNtXXnwSOCEhAYGBgWjTpg1OnjyJJk2aoGfPntiwYQOcnJzQo0cPqNVqvPvuuxAR9OzZEy1btsSIESOwa9cueHl55fkn19Pr2Lp1awQHB6NPnz6oWbMmzp07B3d3d1SqVAlHjx5FrVq1YDAYMGfOHIwcORLBwcFYsmQJ1Gp1nq+jiNiu2SlTpiAhIQETJkyAVqvFn3/+CZVKheHDhwMAbt26haNHjyImJgYffvgh1q5di507d2LevHl47bXXULhw4TzdE+VJrV69GuPGjcMvv/wCR0dHmEwmODk5Yc2aNdBqtWjTpg0cHBxw/fp1JCUlwdnZGWXLlkVISAgMBgOKFi2K77//Hq6urnm2l8K/SW/3TZo0wYIFCyAiGD16tK1H1tixY1GpUiX07NkTd+7cwe+//4727dtj9uzZuH37Nl544QXbvtLbVl6SuX5ffvklAGDnzp1o2bIlatasibNnz0KlUqFBgwbYsWMH/vzzT3h7e6Nx48Y4c+YM6tevDyBv1u1JiIjte0nfvn1hMBhw//59DBkyBF27dkWNGjVw6tQpzJo1CzExMThw4ACmTp2qdNhP7fLly5gyZQoA4Ouvv8ayZcvg5eWF3bt3o0SJEggICEDRokVRv359fPvtt/jyyy8xffp0aDQaGAwGALDL65eIiIiIiCg38K+lfC79D+IWLVrg7t27aNCgAXx9fdG6dWusW7cOarUa169fx4wZM3DhwgX89ttvqFKlisJRP7vU1FQAwKJFi7B+/Xrs27cPe/bswciRI/H1118DABITE+Hm5oZWrVqhYcOGqFKlCg4fPoyNGzfajpe93ixKl5aWhpSUFJQtWxZHjx7FggULAFiTOXfv3kWHDh2QlJSEli1b4u2338ann36KnTt34tNPP8X//d//wdfXN0/fDM98U3DmzJlwcHDApk2bcPnyZfTs2ROAta5dunTBqFGjcPHiRdSvXx9Vq1bFxYsX4eXlBcB6nvPquc5cx/RE47p16/DSSy/B398f8fHxAAA/Pz8cPHgQ5cuXh5ubG1q2bIlz585lact5tY5AxrU2adIkbN26FT/99BM8PT1x8uRJTJ8+Hfv27UNsbCwAYPHixahQoQJSUlJQuXJlfPbZZ7bhwxwdHZWsRrZZsWIFxo0bh82bN6N+/fo4dOgQzp8/j/HjxyMoKAgrVqzAtm3bkJycjHLlymH16tX4/PPPERUVhVOnTsHPzw8AUKxYMTg7Oytcm6eXud0vXLgQAHDp0iW89tprGD9+PLZv325LblSuXBlTpkzBkCFD4O3tjZiYGJw9e9a2r7zY7h9Vv2+//RapqanYs2cPihcvjlq1atnef1u1aoVPP/0UAJCcnIxz584pFXq2ST8vr776KuLj49GuXTs0bdoUPXv2xNy5c6FWqxEfH48NGzbg+vXrOHjwIKpVq6Zw1E+vUqVK2LVrF86ePYuhQ4eiZs2a+O677/Duu+/ahhpTq9Xo27cvPvzwQ7z44ot47733UKdOHfTt21fh6ImIiIiIiPI45TqJUG6Jj4+X5s2bZxmWZcGCBeLg4GAbpiMlJcWuJ5NNH4JjxIgRUqpUKSlWrJiUKVNG3nzzTWnYsKGsXLlSjEajrF+/Xj777DMREdm6dattCKKFCxdK/fr15fPPP8+zQy09KYPBIK+88or8+uuvcuPGDZk/f760bt1aVqxYISIitWvXljZt2ti2//jjj2XVqlVy+fJlqV+/vvTp00ep0J9I+rAtQ4cOtZUdOHBARKwToNetW1d69OhhW5eSkmL7uVq1ajJ37txci/VZPaqOq1atsk1k3qNHDylbtqzExcU99LtvvfWW9O7dO9dizQ7Lli2TgIAAiYyMFBGRU6dOSZcuXeTDDz+01fnfhtp5cCgbe7Vq1SopWbKk/PXXX5KYmCjx8fGi1+ulbdu2MnjwYBERmTx5srRv3142b95sG7Ln7t27UrRoUVm4cKGS4T+3R7X7vn37ipubm+29eseOHfLSSy9lGYrKaDRKTEyMlC9fXn799ddcj/tJPap+vXr1EkdHR+nZs6etrEWLFuLv729bTk1NFRGRzp0724aas3dJSUnSsmVLCQkJsZV9//33olKp5NixYyJiva7T624vMr9H7d+/X1q2bGlrkxs3bhRfX185f/68REVF2ep24cIFWbNmjYhYJ0cvU6aMHD169KH9ERERERERUQb21MiHJNMT9iICo9GIU6dO4cCBA7byYcOGoV69ejhy5AgA61Pt9vqks2QavqZUqVKIi4tD8+bNsWvXLsyZMwf79++Hh4cHfvzxR0yfPh3vvPMOAKBt27bo3LkzAGDo0KHo27cvevXqlSeHWnoS6ec9ODgYcXFxEBH4+PjgzTffxJtvvolNmzahTJkyqFmzJnbs2GH7vS+//BK9evWCn58fli1bhpCQENy7dy9P9tR41FPOvXr1QrNmzTB69Gh4e3vj559/xqVLl7L02DAajTCbzXB1dc2T9crsUXXs3bs33nvvPfTp0weJiYnYsGEDGjdujBo1aiAuLg6AdbgxAChUqBAsFotS4T+Te/fu4fr16yhZsiTCw8Mxffp0REdH486dOwgLCwPwcI8TEbGdS3u9ZjNbtWoVxo4di59++gmOjo5o1aoV3nnnHTRr1gwigp9//hm//vorJkyYgCpVqmDr1q3YtWsXDAYDvLy8sHfvXgwYMEDpajyz9HbftGnTLO0+Pj4ea9aswahRo7Bx40a0adMG48ePx44dOzB9+nQAgIODAzQaDdq3b49y5copWY3HSkhIQMOGDdG6dess9TOZTIiOjsa1a9fQr18/iAj27NmDYsWKoWbNmkhNTYVOpwMA9O/f327P8YPfSywWCy5cuIDdu3fbyjt37owWLVrg9OnTAACtVmuru72Ijo4GYO159s0336BNmzaIiorC5s2bMW7cOGzduhUXL15EUFAQvvjiC5hMJlSrVs32eTV27Fi89957KFq0KIC82duIiIjoQYmJibh69Wqu/j9DQkIe+rvObDbj2LFjOf7/vn//PqZNm2b7m8tisSA5OdnWE/NBqampSEpKeuz+UlNTs9QlIiICZ86cyd6giYjyIwUSKZSDHjf55tixY6Vr165y6NAh27qWLVvann7ND6ZOnSqFCxeWNm3aSKtWraRLly4SHx8v4eHhUrduXfHw8JAhQ4bIxYsX5eTJkxIdHS23b99WOuxsN2LECClcuLBs2LDB1h6uXLkiZcqUEW9vb1m1apVt28xPwaZPSJpXe+zo9XqpXr26/O9//7OV9e7dW7p16ybh4eHi5uYmY8eOFRFrj4169eplefrZbDbLvHnz5MyZM7kc+ZPT6/VStWpV+fDDD21lvXv3lrfeektERNq2bSsdOnSw9dDo3r27lC1b1jb5rIjI4cOH5ezZs7kbeDbo3r27FC1aVDp16iSbNm2S7du3yzvvvCNDhw6VgwcP2rZLSEhQMMqckT4peHBwsIiIREZGStu2bWXnzp1y584dOXTokPj6+oqTk5M0atRI+vfvLwMHDpRXX31VfvnlF4Wjf34pKSlSrVo1GTRokK2sb9++0rFjR9vyvn37xMPDQ3766ScREfnjjz8e6rGRV9+7TCaTdOrUSRo0aGArmzJlitSvXz9Le65WrZp069bNtty6dWupXbu27XPcXj34vST9/Wvq1KnyxhtvyI4dO2zr2rZtK8uWLcvV+LLLvXv3pG/fvvLmm2+KSqWSkJAQuXXrljRs2FBKliwpp0+fll9//VVq164t69evl1dffdV2zd+6dUvh6ImIiJ5d48aNpVatWv+6TVJSkqxevTrLa+3atWI2m+XmzZsSGxsrCQkJtpder5ebN2+KXq9/aF/3798XrVYrH330UZby8+fPi0ajkStXrvxrLIsXLxYAT/xK70GZ7tixY1KoUCF55513RETkzJkz/7mPihUrPjaeadOmSf369W3fZYcMGSJVqlSx/X1ORESPxp4a+Yhkmnyzd+/eeOutt9CuXTts374d7du3h06nw/jx49GvXz+MGDECwcHBtglH7d3HH3+MX375BZ9++imCgoKwa9cu2wSjmzdvxl9//YX4+HicPHkSPXv2xIgRI9C6dWssWrRI6dCz1ZYtW7B37140bdoUv/76Kw4ePIjY2Fh06dIFDRs2xMSJE/HDDz9g/vz5AACdTmd7KiR9Doa82GMnNTUVr776Kvz8/DBv3jwAwAcffIBbt27h22+/RcWKFfH3339jxowZmDJlCry9vbF3794sPTbUajWGDx+OmjVrKleRf2E2mzF48GC4urpi5syZAIAZM2YgLCwMy5YtAwBs3boVd+7cQZcuXWAwGPDNN9+gadOmqFevnu0J4cDAQAQEBChWjyf18ccfY9iwYRg0aBAA4JtvvkGvXr2wd+9e1KpVC6+++ioGDRoEk8mEFStWYP/+/QCADz/8EEuXLlUy9GxlMBjwxRdfoEyZMvD398eUKVNsE9mPHTsWBoMBQUFBWLRoEbp3746+ffuiW7duWL58OZydnbFlyxZbLx17lZSUBJPJhBIlSiAhIQEigujo6Cz1qly5MgwGA27evAkACAoKgsFgwO+//46//voLQN587wKsPQ7q1q2LGjVqYO3atUhJSYGfnx9Klixp6y1pNBpx//59vPjii7bfc3d3h4jgxIkTSoX+3DJ/L+natSvat2+PNm3aYN26dWjTpg2KFCmCqVOnokOHDnj33Xdx7NgxNGvWTOmwn4nRaMSWLVtw/fp1dO/eHTdu3MCJEydw+fJl1KxZE1u2bMEnn3yC3bt3o06dOkhKSoKnpyeWLFmCpUuX4s6dO0pXgYiI6F+JCAwGw0Ovbt264cyZMzh69Ogj1wPWXqtvv/02vvrqK3z99dcYM2YMVq9eDbVajZdeeglFihRB4cKFbS83Nzf4+Phg27ZtD8Xx448/AgAGDx6MiIgI28vDwwNvvfUWTp06laU8IiIiSy8KnU6HsmXLIiUlxfaaO3fuQ2UXLlwAYO35n1n9+vWxdetWrFq1CuvWrUPVqlVx9epVRERE4Pbt21i3bh0AIDIyErdu3cKNGzdsf8s8KDk5GXPmzIG/v7/tu+z48eNx/fp1W+9eIiJ6NCY18pFHTb7ZpEkTdOjQAWFhYZg2bRqGDBmC5ORkFC5cGPv27UPFihUVjvr5nThxApcuXcK+ffuQmpqKK1euAAD27NkDBwcHuLm5YcaMGahatSqaNm2KFi1aYPfu3di7dy+mTJmicPTZ5/r16/jzzz/RtWtXrFq1Cl5eXpg3bx7q1KmDatWq4fvvv0f37t3RtGlT7NixwzZ5uEqlyvPDFYkISpcujdKlS+PEiRMQEbi6ukKn0yEyMhIAbF+A04crcXNzg4eHB/766y+cOnUKQEbiJi/SaDQICgpCQEAAlixZAr1ej4CAAJQoUQK//fYbAOvN31u3biEoKMj25TotLQ3FixdHcHCwkuH/J3mge3jZsmXh5uYGk8lki33evHlo164dRowYAaPRiNq1a2PgwIEwm80oVKgQpkyZgmXLltnlpMGP4+TkhOPHjyM2NhYdO3ZE9erVsWvXLjg5OeHu3bvo378/1qxZg2PHjuHAgQPo1q0bWrRogcTERFy7dg06nc7uhujJzGKxoGjRoti3bx9+++03TJgwARaLBVu3boVer0e3bt0QGRmJoKAgjB49GsOHDwcAvPHGG6hSpQp++OEH2+Toedm4ceMQEBCA/fv34/vvv0e3bt3QqVMnrFy5EsuXL0f58uUxZMgQjB8/HgDQrl07uLi44PTp03j55ZcVjv75dejQASkpKejXrx969uyJgQMHYu/evZg+fTrGjRuHokWLwsfHBwcOHLCL8/kgEYG3tzfq1q2LVatWYcOGDYiMjMSoUaNw6tQplClTBjNmzMD7778PjUaDU6dOwd/fHx06dMCmTZtQrFgxuLi4KF0NIiKif3XmzBk4Ozs/9Bo2bBgAoFGjRo9cbzAY4ODgAACYPXs2du/eDZ1OZ3v47K+//oLZbLYNL5v+MhgMeOutt7LEICKYO3cuXn/9ddy6dQu+vr6oXLkyqlatiurVq2PHjh0YOHAgqlevjqpVq6Jy5cqoUKGC7W9GwPrAiUqlgpOTk+31qLL0mB/1Xfvll1/Gb7/9hm7duiEtLQ0//PAD3N3dUapUKRQpUgQAULJkSXh7e2Pz5s34+eefH3lM58yZg+TkZEyePNlW5uPjgzFjxmD06NG2xAoRET2CAr1DKJtERkZKSEhIlok2ExMTH5p887vvvhO1Wi3Hjx9XIswcZzKZJDk5WUREunXrJps2bZINGzbIgAEDpHbt2pKQkCDXrl2T6tWry+LFi+Wjjz6ScePG2SbgzC8Tcer1evn666/l8OHDImLtluvl5SXe3t5ZJpS+c+eOzJ49W9q2bSvTpk1TKtwnlt7tNjExUQYPHiz9+vWzDSH1/vvvS6dOneTAgQNSpkwZmTRpku332rdvL/369cuyj7wq8/AsK1eulL59+8rChQvFYrHIli1b5M0335R58+ZJiRIlstSxbdu20rdvXwUifnYzZsyQ8+fPP1Se/v4UHh4ubdu2la+//tq2zmw2y4QJE8TR0dHWvu1Z5vec9HMfGxsrfn5+MmTIENuwcGPGjBFnZ2fp0KGD7N69W5o0aZJlaLEjR448cp/2Ji0tTUREbt68KU2aNJERI0bYrtmgoCBRqVQPtfvevXsrEuvT+vzzz6Vbt2629+BFixZJnz59ZO3atSIisnr1aqlQoYJ8/vnntt95sH55/f0rs4iICDlx4kSWtpmSkiJt2rSxTQAuInLgwAFRqVSya9cuJcLMEcnJyVK7dm05f/68/PDDD1KxYkW5cOGC/Prrr1K/fn1p3LixuLm5Sbly5aROnToydepUWbRokYhkXANERER52YULFwSAnDhx4pHrHxwqau7cuaJSqUTEOoQsAPntt99k586d4uTkJHFxcZKUlPTYz0GLxSLJyclZ/lbauHGjAJBRo0Y9cz3WrVsn5cqVy1L21VdfPVR2+fJlASBhYWG2suTk5Ie+dycnJ0uxYsVkxowZIiLy66+/SvqttrS0NPHy8pLOnTs/FEdwcLA4ODjIlClTHlqXmpoqdevWlbJly/7ncFpERAUVkxp26uzZs+Lj4yNNmjQRnU4n8+fPFxHrl4XSpUvLnDlzsmzfokULWbx4sRKhZqsHv0BknhPizJkzUqVKFTl9+rQ0b95cvL295dtvv5V3331XNm/eLL1795Zly5bJr7/+KkOHDs0yD4E9SkhIkNmzZ2cpSx+HMz4+XmrXri29e/eWwMDAh24A3rlzRyZMmCBt2rSR+/fv51rMT8tsNovFYrGd98TERHnnnXekX79+tpu7I0aMEI1GI59++qnt9x68KZgXb/impaXJ/v37H7lu1apV0q9fP1ti46effpIqVarIF198YdvGHm983rt3T/z8/GTfvn22MoPBIA0bNpRmzZrZxtbv1q2b9OnTx7bNtGnTxNXVNd8kZlNSUkRExGg0ikjG3EcxMTHi5+cnXbp0ERGRu3fvysCBAyU0NFSSkpJk/vz5jzzP9nDu/0v6H6s3b96Uxo0bZ0lsbN261bZdXm/3md9r7t27J02bNhWVSiUjRoyQmJgYEXk4sfH333/bfiev1+/fnD17Vry9vaVp06ai0+lk3LhxImI9t1WqVJGJEydm2b5jx44ya9YsJULNNuntNjExUURErl+/LhcvXpTChQtLaGiobN68WerXry83b96UWbNmSdmyZaV79+7y66+/ZtlPXvyMIiIietCZM2fE1dVVTp8+LefPn5chQ4ZkmaPSz8/PNs+EiMj69eulevXqkpKSkiWp8eabb9rmEdNoNP85H8X3338vIta/f8uWLStardaW1Ni3b5/4+PhI5cqVpUaNGuLv7y8BAQFSo0YNqVy5spQuXfqhuavWr18vAMTR0dH20mq1D5U5ODg8lNQoUaKELa7M86WNHz9eSpQoIUajMUtSI/3nP//8M0sMiYmJ4u/vL3Xq1MlyTyOzW7duyQsvvCBeXl6yc+fOpz5fRET5Xd4di4Ue69y5cwgMDMSwYcOwadMmrF27Fu+//z4uXryIQoUKYejQoTh06BB27Nhh+x1HR0doNBoFo35+ImIbYit9uJrMc0JUrVoV+/fvx40bN3Dt2jVERkbi6NGj0Ov1KF++PO7cuYNr166hXbt2mDFjBry8vBSry/PS6/WoX78+Jk6ciMGDB9vKHR0dERcXh6CgIFStWhXr1q3DihUrcPr0aVy5csV2rEqUKIH33nsP69evh6enp1LVeCyj0Yjo6Gio1WqoVCqoVCrcvXsXrq6umD17NpydnTFv3jwEBwdj/vz5OHLkCD755BMA1mFbPD09bWOZWiwWW7vJS6Kjo/Hee++hZcuW+OKLLxAcHIxTp07h0KFDePHFF9G6dWscO3YMixcvxuuvv479+/dj3LhxAB5dx7w8tFY6T09PODg42IYDS0tLw4gRI+Ds7IwWLVpgy5YtOHLkCEaPHo3o6GgcOHAA48ePx4oVK3Do0CHUq1dP4Ro8v+PHj6NTp064ceMGHBwccPbsWcybNw9xcXEoUqQITpw4gbNnz6JXr14QEfzxxx8IDg6Gi4sLRowYAbVa/dBwcfZw7v+LVqsFAJQpUwZff/01Tp8+jffffx+xsbFo27YtAPto9+nvNUlJSfDy8sLEiRNRpEgRfPXVV5g7dy7u37+Pd999Fw0bNsS+fftsQ08B9lG/xzl//jwaNWqEkSNH4qeffsL27dsxffp0nDp1ClqtFsOGDUNwcDA2bdpk+x21Wm039cvMYrHg4sWL+Pvvv6HVahEaGoru3bvjl19+QdmyZVG5cmUcPHgQlStXRufOndG9e3eUKVMGqampGDZsGBo2bIhZs2Zh586dtms5L35GERERPahmzZpITExErVq1YLFYcPv2bVSqVAmffvopDAYDVCpVlr8te/XqhZCQkIfmpNBqtYiJiQEAhIWF4dq1a7h58yauXbsGAFizZg1u3ryJGzdu4NKlS2jVqhUAYPjw4TCbzejYsaNtX02aNMFff/2FkJAQBAcHIzg4GGfPnsW5c+cQGhqKq1evolSpUln+/yKCcuXKZZn3Y+7cuQ+VPWropx07diAkJATDhg3LMixVly5dcPfuXezZsyfL9l999RXq1q2LRo0a2cqMRiM6d+6MiIgIfPbZZ7h169ZDc4BERETAYrFg48aNKFeuHNq0aYOePXva5ighIiLOqWF39Ho9+vfvjwoVKmD06NEoUaIE3nzzTTRr1gxXrlzByZMn0aJFCxQtWhQzZ87E66+/bveTbwJZExrjx4////buPK7G/H38+Ctp35UlIlJ2Q4VIUbKOfZ0hUw0GDTIhexKyC4kZQ9YsY99KiCH7voaxJyZr1igt9++Pfp2vhvl85mOW02mu5+PRY8w5nbvr3X1X93lf7/d14ejoyMyZM4HcyYDMzEz09PR48uQJc+fOZf78+VSuXJlbt25Ru3ZtMjMzmTx5Mj179gTAyMhIbWP5s16+fImLiwv9+vXj1KlTREdH4+/vr3p++/bt1K1bl+joaACMjY159eoVjx49yjdxUrJkSaysrP7x+P8bRVGYPHkynTp14sWLFwD4+Piwbds2cnJyMDQ0ZObMmarExtGjR1WT3Zo0KWhqaoqrqys5OTlYWFgQEBDA119/TfPmzdmxYwdffvkljRs35sSJE0RGRmJpaQlo1hjzLFq0iNWrV6OtrY2TkxOPHz8GcpujDxkyhH379tG7d2/MzMxYvXo1+/fvR0tLiz179nDr1i1iYmIKbIP3Pypv8jInJ4fSpUszfvx49u3bh6enJ8WLF8fc3JysrCzMzc05duwYx44dY9KkSfTu3VvVMDvvGAX9fP9ReUnW8+fPs3nzZnbv3s21a9coV64c0dHRnDt3jpkzZ/Lq1Ss6dOigMdf91KlTcXJyYvfu3TRp0oR58+ahra3NokWLmDZtGs+ePcPf358GDRpw8uRJli1bxueff46VlZVGjO+3Xr9+zcCBAylVqhRDhw7F3Nycpk2b4uHhwfXr14mPj8fd3Z2yZcuycOFC3N3d6dOnDwcOHFAlrDTJvXv3CA8PJyoqil27dtGkSRNevXrFmjVr2LJlCwClS5emSJEirFy5kpEjRxIXF8fVq1cxMzMjICCAdu3aUb16dY05x0IIIUSevAU6iqKwbds2Nm7cSHJyMrq6uqSlpf2hnp3h4eHs27eP+Ph4HBwcKFeuHDY2NpQpUwYAS0tLbGxssLa2xsHBAVNTU3JycsjOziYyMjLfe/n4+HgMDQ3R0dGhaNGiaGtrq/6rra2Nnp4e9+/fz/f1/0w/SUdHR6pXr46VlZWq5wbAZ599xtatWz+4txk2bBhhYWH5HgsJCWH//v1s2rSJTp06UaFCBcqWLfvRDycnJ1asWEFgYCDW1tYfJIiEEOJfTW17RMQnefnypbJgwQLFxcVFGTt2rKIoijJ37lxFS0tLadCggaKrq6sMGjRIiY+PV+Li4pSvv/5aCQsLy9djQ5MNHz5cqVWrlrJkyRKlSpUqH5TZev78uaoMhI+PjxIYGKgEBwcr3377baEoXfPixQulatWqSnh4uJKcnKzY2toqS5cuVapWraoqTZRX0kZR/q/MTfXq1TVqy+r+/fuVoKAgxdvbW2nbtq3i6uqqeu79Hhv+/v6qc9u2bdt8JYs0oWzLwYMHldq1ayuKouSrLX/o0CHVv5cuXap88803yoIFC5TmzZvn66GhCWNMT09XvvvuO6VKlSrK4cOHlbVr1yoNGzZUkpKSPvjc5ORkZeTIkYqNjY0SGBionD17Vnn8+LEaov57ZGdnK6dPn1ZSU1OVL774QjE0NFQmT56sek5RFFV/oNTUVKVatWpK3bp1f3dLemGwdetWpXjx4oqHh4fi4uKi2NnZKXv27FEURVHu3LmjuLi4KCVLltSYn+1Lly4pvr6+ipaWlmJra6sEBwcrDx48UFauXKk0a9ZMsbW1VQYNGqQ8e/ZMURRFiYiIUGrXrq20b99edYyCPL6PSUtLU6KjoxVXV1fl22+/VRRFUebPn69oaWkpjRs3VgwMDJSePXsqe/fuVY4ePaoEBAQoc+fOVS5fvqzmyD/djh07lNatWyumpqbKjBkzFEVRlHHjxil+fn5K69atlXr16qnKQcbGxir79u1TXFxclKVLl6oxaiGEEOLPS0pKUvr27avo6Ogo/fv3V5VizMzMVLS1tZX9+/d/9HXvl59SlNzy2HklNzt27KgEBQUpmZmZCqBs375dCQgIUPVHzJPXe8PX11dVfiorK+s/3iu/e/fugzKPixYtUsqVK6e8evVK9TFr1qwPHjt//vwH5afyhISEKF5eXoqiKMq1a9eU8+fPK1euXFGuXLmifP/996rX5X1cuHBB9f7n3bt3qlLKjx49Up4/f650795d8fDwyPf1V6xYoWhpaeV7fy+EEOL/FFVvSkX8r0xMTPD29sbAwIDIyEh+/vlnbt++zYEDB6hTpw43b96kUaNGqrIXLVq0UHfIf5ndu3czY8YM9uzZg5eXF6ampowYMQKAwMBAIHf1+/t0dXUZNWoUEyZMoESJEv94zH+lnJwc/Pz80NfXx9/fnypVqjB9+nSqV69ORkYG3t7ezJgxg127dqlKj+Wt5Hj9+jWvX79WZ/h/2I4dO7Czs2PYsGG4urry6tUrLl68qHo+LS0NLS0tjI2NmTlzJoMHD6Zr1664uLiwfPlyoOCuclb+/46j7OxstLS0qFatGvr6+uzcuZMZM2Zw+/Zt9PX1CQsLIzw8nDZt2uDn50dWVhaLFi3C2tqaZcuWAQV3jL+lp6dHSEiIqjSeg4MD586dw8PDg7Zt21KzZk26d++Ovr4+NjY22NjY4OLiQnh4uLpD/8stWrSI5cuXM3PmTBISEnB2dubq1aukpKRgbW3N7NmzcXBwoEWLFlhYWHDs2DGcnZ1p06YNsbGxGl9C8LfevHnDsmXLmDRpEn379uXevXv88MMP9OjRg127duHo6MiGDRuYMGECo0aNAgr2db9kyRLWrFnDihUrKFasGNHR0cTExLB37158fX3p3r07T58+ZcmSJWRkZDBjxgwGDRqEtbU11apVA3J/RxTU8f1WSkoKL1++RFtbW3VfMnPmTJycnEhJSeHIkSM4Ozvz7NkzHB0dsbCwICIigvr166s79E+WnZ2NtrY29vb2HD9+HAcHB/T09Hj58iXBwcF4enqSmprK1q1b0dPTA6BVq1ZcvHgRS0tLOnbsqOYRCCGEEJ/u+fPnFC9enMjISHx9fUlISAByS8omJSWRnZ2Nra0tWVlZQO7fzfT09I/ew1pZWXHv3j3u37/Ptm3biIiIyPe8jY0No0aNYsyYMdjb2wN89Dh5OzJ+z/slovK8efOGu3fvYmJi8sFzH3vsvxk6dCh79uxBV1cXLS0t1fjz7nkURSErK4vevXsTERGBjo4OtWrVAlCVxL527Rpubm4YGxurjpuamkqpUqXy7QgRQgjxfzTjnbMAcm8KIHfivmPHjgwcOJCUlBTc3d1xd3fHwMCAGjVq4OvrS1xcHG/evFFzxH9e3g0B5N7YjBw5kh9//JGTJ0/SuXNnZs2axfz581UToHn9FwD69u2LlZUVBgYGhIaGYmtrq5Yx/FWKFClCv379sLS0xNDQkIULF1KlShXatWvH9u3bOXLkCGvXriU6OhodHR1Vv5HLly9jZmZG2bJl1T2E3/X+tRoXF4ePjw/9+/enePHidOvWje+++07VZ8PLy4vk5GQURcHQ0JCIiAj69+9PaGgoUHAnPTMzMxkzZgyJiYloa2uTkZFBsWLFsLa25unTp+zbt48zZ86wcuVKGjZsyNOnT1Wv7dOnD8HBwUyaNAnQrIlPAHNzc4YMGULnzp05fvw4hoaG+Pr6kpyczOjRo3FxcaFdu3asXr2aBw8e8O7dO+DPbQ0vKPbu3cuuXbsA6NevH+XKlcPNzY3g4GB27NihSs4GBwcTEhJC9erVVW++TExMSEhIoFKlSoUuoQG5Sef79++ryszZ2NgwfPhw3N3dOXToEIqiYGNjQ0REBDY2NkDBLr3VtGlTDh06xKZNm5g6dSrdunWjUqVKlChRgkmTJrF8+XJ+/fVX1q5dy8mTJ/Hz8yM9PZ0uXbpQuXJlQHN6KyQmJlKvXj169+6Nm5sbQ4cOpWXLlgQGBpKTk4OTkxP169dHR0eHEiVK0K9fP44cOcKrV6/UHfqfoq2tzd27d6latSrjx48nLCyMQ4cOsWXLFmbMmMGDBw/YsWOHavIFcktGPnr0SPV7TQghhNBUtWvXxtDQEF1dXRo2bMioUaNU7zvz/vZVqFBB9Zi+vj7m5uaMHDlSdYy88lXr16+nXbt2LFq0iFKlStG7d+98X2vAgAFYWVkxceLE/xjTzZs3Vb08rKys8n2YmZl9tOz0gwcPcHZ2RlEU1ce8efOwtbXN99j169f/0PdlzZo1vH37lhcvXvD8+XPWrl0L5CaBnj9/zosXL0hLS/ZayG4AACdZSURBVPsgcZMnOTmZs2fPflAu/N69ewX6PbwQQqib7NTQEFlZWRQtWpScnBzu3r2LgYEBfn5+AMydO5cxY8aoajU+ffoUOzu7j65K0CRZWVn07NmTypUrExoaSrVq1ahbty7Fixdn8uTJTJw4kfbt26OlpcXQoUMBGDJkCK9fv2bLli307NmThg0bAh9foaEp3r59y8GDB2nevDlNmjTB3NwcgNWrV3P48GG2b9/OlStXGDduHKdOnWLixIkYGxszfvx4FEWhSpUqBAUFFdikzsOHD5k7dy6NGzemRYsWREZG4ufnR2xsLI8ePeLu3bvMnz+fzp07k5iYSO/evalatarq9QYGBgwbNkxVn7+gTnreu3ePzMxMIiIiGDBgAJcvX1Y1xitZsiSbNm3i8uXLLF68mFu3bjFu3DjatWuHhYUFkNtLIy+xqSkTn++zsLBg4MCBpKenM2PGDOrVq0dISAjPnz8nOjqaffv2sW/fPkJDQ1UN8ArqufyjUlNT+emnn8jOzubFixfo6OgwduxYtLW1sbOzw9TUlDFjxtClSxdVT4kKFSoAuUmwY8eO4e7uzrx584D8vYU00W/jL1q0KK6urjx8+JD79+9TpkwZTE1NMTQ05PHjx6rPLYi1gz92LsqVK8ecOXNYunQpnp6ejB07lpCQEMqXL0/58uVJSUlhzpw5mJmZER8fz+zZs1Vj05SklaIoZGdnM27cONq3b09oaCi3bt1i2rRpvHz5ks8//xyA2bNn8+2337JgwQIAkpKSKF++fKFYaWhjY8Pp06dxdHQEcicspk+fztmzZ5k/fz76+vo8evQIRVHYvn07169fZ/jw4axatQozMzM1Ry+EEEJ8usOHD6Orq0vRokXz3Qc9efIEV1dXhgwZoqosALmLMt++fYuBgYHqcytWrMiqVauIiIigR48eVKhQgTFjxqCnp5dvQaOhoSEBAQGMGzeOcePG/W6vjryvdebMGcqXL5/vufj4eLp16/bBa65cuUL16tU/+fvwvk2bNjFq1CguXrz4yfc5Y8aMoWTJkrRq1Srf4/fv35ekhhBC/AeS1CjgcnJy0NLSUiU0XF1dMTQ05MWLF4wdO1aV2JgzZw6mpqbo6uqyZcsWjhw5otET+ZBbZqhevXrExcWxcOFCTpw4gZGREYMGDSI1NZVp06YxduxY2rVrB+Q24dLS0iIwMBAfHx/s7e1VWz41eSJw48aNhIaGMm3aNHR0dHj27BmjR49m2rRpbN++nUuXLvHFF1/g4+NDaGgohw8f5siRIwAsWLCALl26qBqkF0QvX74kJSWFbdu2ceHCBW7dukXLli15+fIliYmJVK9enSZNmrBw4ULKlSunKkMDMG/ePKpVq4aXl5caR/DHVKhQAX9/fzZs2MDs2bMpVqwYNjY21KlTh9OnT5OUlMTUqVOpXLkyWVlZVKlShbVr11KpUiWqVq1K6dKlNWbi8/dYWFgwYsQIcnJyGDFiBGlpaXTp0oWBAwcycOBAdYf3lytWrBgDBgwgOjqa+Ph4jh8/zrt37yhRogRbtmyhRYsWLFmyhNu3b9O5c2cWLFhA1apVMTc3p3nz5pQsWRJ3d3fVBLom/x7LG8OBAwfYv38/tra29OzZk+bNmxMYGIiRkRFOTk6qklx169ZVd8j/Ud65WLt2Lebm5rRs2RIANzc3duzYwcaNGwkODiYoKIjp06ejp6eHu7s7jRo1YuLEiTRu3Fi18lCTklXv3r1DT08PAwMDHBwcsLS0xNLSkg0bNvDo0SOioqLo2LEjQ4YMITw8nOHDh2NlZcW6des4cuSIqiSTphk7diwnTpwgOzubBg0a0L9/f9V5u3fvHrdu3QJyV2pGRUWRlJRERkYGZmZmGBsb06dPHxwcHNQ8CiGEEOLPyWvk/b7Lly/TrVs3atSoQZcuXahVqxZdu3Zl3Lhx+co/P3/+HMhtAn7+/Hkg9+9rdnY233zzzUe/Xr9+/VS7XSdMmPDRz/lvi6B++3xWVhYHDhxg5syZ//F1/83bt285f/483t7ezJ0795MSGoqiMHr0aKKjo1m/fv0Hx7h27Rru7u5/Kk4hhCjMJKlRQD1+/JjixYtTpEgRVWKje/fuWFlZMWHCBMaPH0///v3JzMzEz8+PIkWKMGLECB4+fMjJkyepUaOGuofwp5mZmREQEICtrS2DBw/G3NycS5cuAdC5c2c2bdrEsmXLCAwMpF27dmhpaTFo0CDOnz9P1apVKVWqlJpH8Nfo2bMnjx8/ZsKECdja2tK4cWN8fHzw9PRk5cqVhIWFcerUKRYsWMDGjRtVtfcnTpzI1KlTVZNtBZWDgwNjx47l+++/JyUlhcOHD5OSksLt27c5ffo0JiYmfPPNN/j4+GBkZET37t1ZvXo127ZtY+rUqar+IZrAzs6OsmXLUqFCBWJiYjAzM8Pe3h5tbW2CgoKws7MjPj6enj17oigKiYmJvHz5kr1796o79L9MXrklHR0dJkyYQGZmJt27d1c9r0kTvB9z8+ZNSpUqpdrqXqtWLe7cucPp06fx9vZGR0eHL7/8ki1btrBp0ybCw8M5fvw4+vr6hIeH079/f54/f07ZsmVZsWIFoNlJ2TxaWlps2bIFb29vGjZsiImJCUWLFqVVq1ZoaWkRFRXF0qVLKVeuHBUqVCAgIEDdIX/U+9dneno6ISEhVKpUiVWrVrF48WKqV6+On58ffn5+tG7dGicnJ0aOHMmMGTM4d+4cjRs35tixY/l2zmnK+U1JSaFFixYkJCRQqlQpoqKiGDx4MJBbNsHFxYUHDx5w7tw5wsLCVPclN2/e5OTJk9SsWVPNI/h0bdq0oUyZMiQmJmJsbEyZMmXQ0tJi0aJFhIWFcfbsWcLCwqhZsybe3t5cuXJF1TepaNGin1SfWwghhCiosrOzOXDgAMuXL2f16tV06NCBZcuWoaury8yZM5kwYQLLly9n6NChDBs2DGNjYzIzMz84TsuWLalQoQJmZmZERERw/PhxIHeXBuQmQE6cOPHBrorMzEzVLv28crV5u51/K2/Xe54tW7bw+vVr1cLI98f0funbrKws7t+/D/BBsuHVq1dER0eTlpbGpk2bPthh8UecPXuW4OBgdu7cybx58+jcuTOQW5Z51qxZqvfCQUFB//OxhRDi30Kza3sUUhkZGdStWxd/f38gd3VBZmYmKSkp9OrVi6pVq2JiYkKrVq0ICgoiNjYWHx8fwsPDuXbtGs7Ozmoewad58+YNGRkZAERERHDmzBmKFi3Kjz/+qLpxmTZtGgMGDCAhIQFzc3OePn3KmTNngNybosWLF3P//n3Wrl37wfZTTZF3g/b+/3t6emJkZERiYiIODg4kJyezaNEiRo0aRY8ePcjMzGTx4sW4uLiwatUqBgwYwLRp00hISMhX27ugqlixIo8fP8bU1BRvb28GDx7MmTNncHJyYsmSJaqmat7e3lSsWBEXFxeCg4OJi4vjs88+U3f4f9iRI0eYOnUqd+/epUWLFty6dYu0tDQyMjKIiYkhPT2dGjVqEBMTw82bNzlz5gwHDhwosKXDPlVejw0PD498TeBBcyZ4PyYlJQUfHx8OHjwIwPXr18nOziYmJobo6GhevnxJcnIyDx8+xN/fn06dOnHlyhUqVqxImTJl+Oabbzh16hRVqlRRJTQKQ18RyP39vmLFCsLDw9m9ezd+fn4sWLCAqKgoatSowfr16zl+/DirVq1i3bp1QMEce971GRERwYIFC3Bzc6Np06Y8fvyYypUrs23bNtq2bcuIESMICgriwYMH2NnZMWLECF69esWbN29UP8+//V1fUOXFmZ2djb6+PtevXyc4OBgDAwO+/vprILfHxsiRI3ny5AkxMTGEhITg7OzMzJkzuXHjhsbel+SpX78+/v7+REZGMnLkSLS0tJgzZw6HDx8mMTGR8uXLk5qaSnJyMpaWlri5udGwYUMsLCwkoSGEEKLQOH78OG3btsXa2hovLy9u3rzJxo0bWb9+PUZGRujo6PDVV1+pSiPPmDEDBwcHEhMTP5rUcHNzU/XSePPmDadOnaJ3796qagsANWrU+OD9QXp6uuo+MW/+IDExkVevXuX72L59u6qsLeQmKsaPH0+HDh2wsrLKd8yMjAzVsSB3EaWHhwdVq1b9YF7BxMSEr776ip07d340oZE31t+7l3306BFdunThzp07/PzzzwwYMED1nKGhIadOneLkyZNMmzaNrl27fvQYQgghQEvRlHfV/zIJCQl07NiRr7/+WrU10tfXl5CQEKZOncq9e/eIjo6mZcuWnDp1ilmzZhEYGKjmqD9dRkYGISEh9OvXj3Xr1rF8+XISEhK4e/cuS5cuxdfXl86dO6OlpYWRkRHjxo3Dzc2N5cuXs2nTJk6cOKHaWprXf0QTvb8K+OrVq1SsWJHMzEz27t1Lamoqr169YvPmzVhYWPDZZ58xYMAAkpOT+frrrxk+fDh79uwhNjYWS0tLVq9eTa1atdQ8oo+7ePEi2dnZ2Nvb8/TpU0qXLk3r1q3JzMykQYMGqrJEH9tuO3HiRDZu3Mi6deuoVKmSGqL/dO/evWPHjh0sXrwYLy8vSpcuTVxcHE5OTiQlJVGzZk26dev20YZ2hdGbN29UK7EKi4kTJ3Lw4EE+++wzTp06RUhICHZ2dkyfPp2cnBxMTU1JT0+nXbt2eHl55fuZ79ChA5aWlkRFRQEFt+n9p2revDmpqamEhobSq1cvHB0dSUlJoU6dOkRGRuart1yQd+yEhoaycOFCWrduTVpaGnXq1GHIkCEEBARw5coVrKysqF+/Pnfu3MHNzU218u7p06dYWlqqOfr/3cuXLzE1NQWgW7duaGtrs2bNGn766Sfmz59P3bp1mTlzpup8zZo1i6CgIMaPH8/o0aM19u/xb71/TT59+pTAwEAaNmxIv379AIiMjOTcuXMsXrxYnWEKIYQQf6vAwEDKlClDp06dsLOz+4+fm5SUxE8//URQUNBfel/n5uZGzZo1+f777/+n12VlZbFhwwaqVav2wcK4Z8+e8fz5c9WOj+PHj/Ps2TM8PT3/5/KZa9eupXv37qSlpf3ue52UlBSKFy9eaO6ThBBCHSSpUQBlZmaio6PD0aNH8fLyIigoiNDQUCC3WVSbNm3Yu3cvxYoV46uvvqJ69eq0b98+X/NkTdSzZ09iY2OpU6cOmzZtwsDAgD179uDl5YWOjg5hYWFERkZSpUoVPv/8c9VWzIYNGzJx4kSaNGmi5hH8dYYMGcL58+fZuXMnurq6vHr1SrXac9asWUyYMIEZM2bQt29fHj16xKBBg/j555/p3bs35cuXp3Xr1tjY2Kh5FB96+/Ytly9f5urVq6pdN+/evaNHjx7cuXOHtWvXYmxsTFpaGunp6XTo0CHfeV2xYgVTpkxhw4YNf1lzt39adnY227Zt44cffqBZs2ZUrlyZiIgIjhw5QosWLejevTtdunQpsBO6f4eCPIH9Rxw+fJiRI0cyf/58KlSoQOPGjbG1taVGjRokJSXRo0cP7t+/j7GxMSVKlCAmJoaMjAy6du1Ko0aNyMnJwd3dnYoVK+bboaHJCY2PndNLly7Rv39/7OzsVJPBc+fOZe/evWzcuFEj+kBlZWXh7e1Nnz59aNasGZD/XJ08eZK4uDjVJH/Dhg2JiYnJdwxNut6fPHmCs7MzzZo1o1WrVlSqVInBgwezadMmihYtyurVq1myZAllypRhxYoVFClShOnTp3Pjxg1CQkI0Yqfgpxo8eDCXL19mz549AAwdOpRr166xfft2NUcmhBBCCCGEEIWfpIULGEVR0NHRISsri02bNlG/fn0mTpxIVlYWYWFhWFtbqyZVnJycOHjwILNmzcrXhEtT1apVi507dzJ79mzS09OZMWMGCQkJXLhwgcGDB9OkSROWL19O9+7d2bJlC9ra2gwZMgRTU1OuXLmi0UmNnJwc0tLSMDExISwsjD179rBjxw50dXVJTk7GyspKNRHWsGFDSpcuTXR0NFZWVnTq1Ak/Pz9++eUXpkyZou6h/K7Xr18zZMgQnj59yqJFi+jRowfDhg2jdu3aPH/+nFmzZtG0aVOqV6+OgYEBK1asUJ3nxo0bs3r1aoYOHcq+ffs0NqEBoK2tTdu2bQFYvHgxz549w9jYmPDwcBo0aECJEiU0ZsLzr6LJ41UUBRsbG8zMzDhx4gSxsbHcu3ePWbNmsWXLFvbu3UupUqVwcnLC0dGRSpUqqfoRbNiwgZycHOrUqUODBg1Uu/IKS0Jj165drF+/nmLFiuHv70+NGjU4ePAgjx8/5s6dO9y9e5dly5bh5OSkEQkNyE1qXL16lXPnztGsWTOysrLYvn07586dIycnh06dOhEcHEzTpk0JCwsjNjaWlStX8tVXX6mOoUnXu4mJCXPnzlUlapKSkkhLSyMuLo4uXbrQt29fypYty6RJkyhZsiT29vbcuXOHY8eOFdqERt713adPH/r06UNSUhK2trakpaVRsmRJdYcnhBBCCCGEEP8KslOjgOrUqROvXr0iKCiICxcuEBoaSt++fZk1axbx8fHMmjWLtLQ05s6di6Ojo7rD/dOOHz9OQEAAaWlp9O7dG0tLS06ePEl6ejppaWk0atSI/v374+bmRrNmzXBwcGDRokV06NABNzc3TExMNK4UUZ6cnBxOnjxJWFgYRkZGXLp0iaFDh1KvXj1OnDjBunXrWLFiBVZWVmRnZxMaGoqrqyv3799nyZIlqu28edeKvr6+uof0u6Kjozl8+DDm5uZcuXIFGxsb5s2bR8uWLSlXrhyLFi1S7VRKTExk6dKlvHv3js6dO1OzZk2ePHmisef5t7Kzs9myZQuTJk2iVq1aTJ8+vVAkJ/9tnjx5oqrJ27t3b1JSUmjYsKFqJfuxY8dYuHAh8fHxjBkzRvX7+sqVK/z444/cunWLyMhIypYtC2h+QiNPbGwsnTp1wtvbGzc3N1XvBcjdsfHll18CUK1aNVUPDU3ZwbBmzRq8vb2pW7cu165dU/3cli5dmoMHDxIbG0vz5s1JSUnh+vXrNGrUSM0R/3lZWVloa2uzceNGNmzYwLp162jdujUVK1bE19eXmjVrcujQIfT19bG2ti50fYA+5pdffsHZ2ZmVK1fSvn17fHx88PX1Ve3gEUIIIYQQQgjx95Gkhppdu3aNq1ev0qxZM1U98devX9OlSxf69+9Phw4dADh48KBq5eewYcNUn2dsbKyu0P+0t2/fcuzYMU6cOEHt2rVxdHTkxIkT+Pn54erqyoABAzA1NWXevHlcu3aNzp07Y2RkRPv27TEzM2P16tWsW7eOTZs2UaxYMXUP55PkTeIlJyfj4uJCqVKlSEhI4Ndff8XDwwMDAwPmzJmjWtkP8PDhQ9Vq0JUrVzJp0iTWrFnDs2fP8PLyUtdQftdve5wcOHCAESNG8OuvvxITE8Pp06c5c+YMLVq04OzZswwbNkyVmLl27RpTpkxBV1eXefPmoaurq65h/C0yMzOJiYkhOztbVXdfaI61a9cSHR1NQEAAhoaGdO7cmc2bN7N+/XpiY2Np2rQpw4cPZ/ny5WzdupVTp04xZcoU2rZtS82aNTl79iznz5/Hz89P3UP5S2VnZzN06FAsLCwICQnh4cOHREZGoigKrq6ufP755zx8+JAXL16okpSalsz5+eefOXnyJCYmJnzxxRfo6elhZGTE4MGDOXjwIEePHs1Xf1nTxvdb7yecdu/eja+vL/PmzWPhwoWkpaWRnJzMpEmT8PX1VXOk/6yTJ0+io6Oj2nFobm6u7pCEEEIIIYQQ4l9Byk+p2YoVK5g8eTK9evWiadOmfPnllxgbG6Orq8vChQtVSY26devSuHFjhg8fTlpaGiEhIRqd0EhLS6NVq1aUKVMGExMTfvrpJxo1akTp0qVJTU3lzJkzHDp0CAcHBy5fvoyzszPHjh2jX79+lClThqJFi9K9e3e6d++OhYWFuofzSd6fJFq1ahUlS5Zk8+bNGBsbk5qaiq6urqq0zePHjylevDgAJUqUUL3WxsaG69evY2ZmhpOTkzqH81Fv376lY8eOREREYG1tzaNHjyhWrBhly5alYsWKDBw4EEtLS2bMmMH333/P48eP0dfXz7fCeciQIRQvXrzQJTQAdHR0aNu2Ldra2oDmrFQXEBUVxbhx42jZsiWHDx/G0NAQf39/AgMDeffuHRcuXGDt2rV88cUXPH36lEuXLjF37lzWrVun6gfk6Oio2rlRmM69trY2r1+/ZsmSJZQoUYKQkBAaN27M27dvOXDgAM7OzpQsWVKVnFUUReMm/D09PfH09ATyJyzc3Nx48ODBB+W0NG18v/X+tdm8eXPs7e158eIFe/bs4caNG6xbt4569eqpMUL1qFu3rurfktAQQgghhBBCiH+OZr/LLgS6du2Ko6MjSUlJzJ49m06dOpGSksLAgQPR19cnMDAQAH19fT777DNGjx7NF198oeao/5y0tDQ8PT1xdnbmyZMn+Pr6snHjRnr27ElQUBB79uzh6NGjrF69mlGjRhESEsK8efM4dOgQNjY2REVFceTIESwsLDQ2oQH/N0k0btw41qxZw7hx4zh79iyLFy/m22+/ZdWqVQwYMIBly5bRrVs3zpw5o3pd3mtr1arFtm3bqFixotrG8Z+8e/cOHR0dHj16xLZt2wgICCApKYmaNWvy7bffYmBgQGxsLOfPnyctLU01Ebh37166d+9OZmYmNWvWpFSpUmoeyd8nL6EBmlVr/9/s4sWLTJs2jdjYWKKionj27BkXL17E3d0dDw8PatWqxdu3b0lKSiI5ORkvLy++/fZbdu7cyblz59DR0SE7OzvfMTX53Odt+MzMzFQ9tnjxYtq0acOdO3cIDw9n/fr19O/fnyJFinwwwa/JY3/27BlTp05l2bJl3LhxgylTpmBlZaXxSYzfk3euPT09SUhIAMDe3p7Ro0dTtWpVdYYmhBBCCCGEEOJfRHZqqFmtWrVo0aIFGzduZPPmzQwYMAAfHx+cnJxo0KABp0+fply5cri6uhITE8PFixcpX768usP+Uy5cuMCpU6eIiIiga9eu+Pj4EB8fT506dfDw8OD169csXryY2NhY2rZty5s3bzA0NOTJkydER0fj7+/PhQsX1D2Mv8TMmTNZsWIFd+7cAaB8+fJkZGQwceJE9PT0OHLkCL/88gsPHjwgIyND9brly5dja2uLh4cHbdq0UVP0/52ZmRnlypVj/fr1hIWFMXv2bPT19QkKCiI6OhpFUViyZAlLly7l8OHD1K5dm2HDhvH27VtevnzJkydPsLa2VvcwhMjH3t6ehIQESpUqxaNHj7hw4QJfffUVXl5elChRgg0bNuDl5UVmZiZ79+4lKCiIixcvMnv2bNUx3k9mabK8HSbbt28nKioKOzs7GjRoQNeuXVm9ejXPnj0jISGBEydOEBwcTOXKlVW7zgoDRVF48OABYWFh1K5dG3t7e+bPn696TpMTNh+TN56uXbtSt25dvvnmG9zc3NQclRBCCCGEEEKIfxtJaqhR3oTH5MmT2bVrFxs2bODnn3/Gzs6OGzdukJ6ezvTp07GysqJevXqMHz9e4xMaAA0aNCAuLo7OnTszadIkBg0axLJly6hatSrNmjXD3t6eOXPmMHjwYNatW0fTpk2pU6cOBw4cIDQ0lJMnT1KjRg11D+NPS09P58WLF+jr6/Pw4UPS0tIoUqQI5ubmXL9+nT179nDu3DmWLl3K0aNHSUpKwsHBgfnz5zNjxgz27dun7iH8R3nXd58+fdi8eTPHjh3jypUr5OTkYGBgQHJyMj/88AMVKlSgUaNG9OrVCwsLC4yMjFAUhVWrVklCQxRIBgYGGBgYkJmZydy5cylbtiy9e/dGURTOnDmDhYUF/v7++Pj48OOPP/LixQt69+7N+vXrsba2pn79+uoewl9GS0uLvXv38sUXXzBy5EiOHz/O/v37ycjIoGfPnuTk5DB16lQgN4m/bNkyoPBM+BcrVoyIiAiCgoLIyclRNcjW9B4a/429vT3du3f/VzQEF0IIIYQQQghR8EijcDXLm9hZt24dixcvJiMjgwcPHjB//ny2bNnCihUr6NWrF1OnTlU1Ty4s4uPj6d27N5UrV6Z169ZERUXh6emJubk5ZcqU4ejRo4waNYpTp06RmprK1q1bmTNnDtWrV1d36J/s7du3qobwkNvsfebMmWzevJl69eoxffp0LCwsmDJlCuvXr6dly5ZMnjyZDh068OzZMzw8PDhz5gxTpkzRmMROYmIiNWvWxNramvbt2+Pq6kq7du2YPXs2ISEhZGZmoqOjg4eHB4MHD6Zjx47qDlmIP+zy5cs0adKEkSNHcvv2bfbv30+TJk3o27cv8+fP5/z58xw8eJDnz5+zZ88eunbtqu6Q/3KTJk3i119/ZcGCBUyaNIk9e/aQmZlJQEAAX375JdnZ2Tx79gwrKyug8E/4F5aEzX/z7t27QtnrSAghhBBCCCFEwVd4ZxU0RN7ER+PGjSlSpAhnz55l//79NG3alMjISJYvX05gYGChS2gANG3alOjoaK5evYqVlRUnTpzAzs6OY8eO4ezsjLOzM7Vq1eLIkSPo6OgQFRWl0QmNe/fu0bx5c969e8ejR4+4efMmxsbGDBs2DG9vby5evEhaWhoAP/74I3p6eqSmpuLp6cmOHTswNTWldu3a/PjjjxqT0ACoUqUK48aNY8WKFfj7+xMVFUX//v0xMjLi6tWrLFu2jGvXrvH69WtVGZOcnBw1Ry3EH1OtWjV27NjBkCFDuHTpEgEBATx+/JiVK1diZGREfHw8kNtEOC+hoelrCX4b/927dzEyMuLGjRv89NNPfPPNN9SpU4eQkBBCQ0PR1tZWJTQ0sSn4/+rfkNAAJKEhhBBCCCGEEEJtpPxUAVGyZEmmTp1KvXr12L17N76+vgCFftW6u7s7S5YswcfHByMjI4oVK8aVK1eoVKkSAG5uboSHhxeKyRMbGxuqVatGTEwM/fr1o1WrVowePZrKlSvTq1cvXr9+zXfffYePjw9r167FxcUFgLlz51KjRg3mzZun5hF8Gm1tbcaMGYOOjg63b9/myJEjjBo1ihYtWjBnzhxatGjBlStXePPmjWqys7BPeorCpU6dOly6dInU1FTc3Nxwd3enS5cujB49Gj09PdVupDyaOOm9aNEizMzM6NatG1paWvl2I/zwww9kZGTw9ddf07p1a3r27MnFixcpU6YMjx8/znccTRy7EEIIIYQQQgghChaZOSxAateuzdixY5k1axaJiYnqDucf07RpU5YvX86gQYM4ceIEp0+fxtDQEGNjY+rUqUNmZqZGr2y+c+cOwcHBABgaGjJq1CiWLl3K/fv3mTRpEm/evMHKygpfX1/s7e05evSoKqkDkJmZSalSpcjKytLY70PehG5GRgZeXl54eHhw//59jIyMqFGjBiVLlmTdunVYWlqqOVIhPk21atVwdXUFwNbWljJlynD79m2AfAkNTZSRkcHly5cJCQlh27ZtAKrEBuQmIQ0MDDA2NubGjRs8fPiQI0eO4OvrS2RkJKD5u1OEEEIIIYQQQghRcEhSo4Dx9vamVKlS/7rJ3WbNmrFy5Uo2b97Mzp070dbWxt7enqCgIIyMjDR6dW9iYiJpaWlMmDCBrVu34uLigra2Nvfu3cPPzw9DQ0PevHmDnZ0d7dq1IykpiQsXLgC5ZaiWLVvGl19+SdGiRTX6+wC5pag2bdpE1apVGTNmDCVKlMDBwYH69etrVEktIT6mSJEiZGVlsXPnTipXrqzabaXp9PT0CAkJoWvXrowZM4atW7cC/5fYUBSFnJwcmjdvzp07d3BycqJYsWKqHYf/lh4TQgghhBBCCCGE+GdIo/ACKD09vVD20Pgjdu/eTbt27Vi8eDE9e/ZUdzh/mTt37tCsWTMmTpxIXFwccXFxhIaG0q9fP5KTk+nRoweurq5MmzaN4OBg9u/fz65du+jfvz99+vShUaNG6h7CX2rv3r3MmjWL2NhYQCY9ReHy6tUr7t69q9E9gD7m+fPnzJw5k23btjFx4kTat28PQFZWFkWLFuX+/fscPnyYMmXK0LBhQ6DwNwUXQgghhBBCCCHEP0+SGqLAiYuLo2LFijg4OKg7lE/29u1bNm/ejKenJ9bW1kyZMgVHR0cqVqxI+/btqVatGi1btsTGxoZx48ZRv3599PT0KFGiBH5+fnh7ezNkyBA8PDwKfYJLEhqiMCts13deYmPr1q1MnDiRDh06AJCSkoKzszNeXl6sXLkSkISGEEIIIYQQQggh/h4y2yAKnJYtW2p0QgNyEzM//fQTR48eBaBmzZq0bNkSX19f/Pz82LBhA0ePHsXPz49mzZoRERGBp6cnly9fRk9Pj7Fjx1K/fv1Cn9AAaRwsCrfCdn2bm5szbNgw2rdvT3BwMHFxcQA0adIER0dHVUIDkISGEEIIIYQQQggh/hZF1R2AEIVRx44dSU9PZ//+/URHR2NpaUmbNm3w9/fn+PHjbN++ncOHD2NgYEDp0qW5evUqd+/exdraGlNT00JXbkoIUXjkJTaKFCnC0KFD6dy5M+7u7sTExACyQ0MIIYQQQgghhBB/Lyk/JcRf7P1yM02bNiUxMZH58+fj6enJoUOH+PXXX1m4cCG6uroULVoUJycnypYtS+XKlWnXrt0HxxBCiILo+fPnhISE8PLlS5YuXQpIQkMIIYQQQgghhBB/P0lqCPE3UBSFkydPsnbtWlJTU9m1axf169fnl19+wdramnnz5pGdnc0PP/zA4MGDmTBhApUqVWLkyJHo6OhIQkMIoRHevHmDoaEhIAkNIYQQQgghhBBC/DMkqSHE3yQzMxMdHR0yMzNxdHTk3bt3fPXVV5iZmREQEEB8fDzLly9n5cqVXLt2DRMTE6ytrdUdthBC/M9kd5kQQgghhBBCCCH+KbKkUoi/iY6ODgDZ2dnUqVMHExMTjI2NKVmyJGlpaWzdupW8nGKlSpUkoSGE0FiS0BBCCCGEEEIIIcQ/RRqFC/E309fXp1evXsyePZv09HQSEhLIycmhfPny9OvXT93hCSGEEEIIIYQQQgghhMaQnRpC/AMcHR3p06cPo0aNon79+uzevRtra2sqVKig7tCEEEIIIYQQQgghhBBCY0hPDSH+Ie/XnF+6dCktW7aUklNCCCGEEEIIIYQQQgjxP5CkhhD/oJycHIoUkQ1SQgghhBBCCCGEEEII8SkkqSGEEEIIIYQQQgghhBBCCI0gS8aFEEIIIYQQQgghhBBCCKERJKkhhBBCCCGEEEIIIYQQQgiNIEkNIYQQQgghhBBCCCGEEEJoBElqCCGEEEIIIYQQQgghhBBCI0hSQwghhBBCCCGEEEIIIYQQGkGSGkIIIYQQQgghhBBCCCGE0AiS1BBCCCGEEEIIIYQQQgghhEaQpIYQQgghhBBCCCGEEEIIITTC/wMwM0RB+hjUVwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================\n",
      "特征重要性分析\n",
      "======================================================================\n",
      "线性回归系数分析:\n",
      "     特征名称        系数     系数绝对值\n",
      "4  历史违约次数 -1.383324  1.383324\n",
      "2      性别  0.393293  0.393293\n",
      "1      年龄  0.168401  0.168401\n",
      "0     月收入  0.000599  0.000599\n",
      "3  历史授信额度  0.000066  0.000066\n",
      "\n",
      "随机森林特征重要性:\n",
      "     特征名称       重要性\n",
      "0     月收入  0.414820\n",
      "3  历史授信额度  0.270905\n",
      "4  历史违约次数  0.207646\n",
      "1      年龄  0.095011\n",
      "2      性别  0.011618\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "梯度提升树特征重要性:\n",
      "     特征名称       重要性\n",
      "0     月收入  0.582768\n",
      "3  历史授信额度  0.216526\n",
      "4  历史违约次数  0.162290\n",
      "1      年龄  0.035622\n",
      "2      性别  0.002793\n",
      "\n",
      "XGBoost特征重要性:\n",
      "     特征名称       重要性\n",
      "4  历史违约次数  0.542783\n",
      "0     月收入  0.243293\n",
      "3  历史授信额度  0.110344\n",
      "1      年龄  0.058815\n",
      "2      性别  0.044765\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================\n",
      "最佳模型: AdaBoost\n",
      "最佳R²分数: 0.7325\n",
      "======================================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "前20个样本的详细预测结果:\n",
      "    实际信用评分  预测信用评分  绝对误差  相对误差(%)\n",
      "0       79   71.06  7.94    10.05\n",
      "1       80   70.97  9.03    11.29\n",
      "2       62   70.97  8.97    14.46\n",
      "3       89   84.73  4.27     4.80\n",
      "4       80   70.98  9.02    11.27\n",
      "5       86   84.73  1.27     1.48\n",
      "6       72   71.06  0.94     1.30\n",
      "7       85   84.73  0.27     0.32\n",
      "8       85   84.72  0.28     0.33\n",
      "9       87   84.98  2.02     2.33\n",
      "10      74   71.06  2.94     3.97\n",
      "11      72   71.06  0.94     1.30\n",
      "12      66   71.06  5.06     7.67\n",
      "13      79   71.06  7.94    10.05\n",
      "14      75   71.06  3.94     5.25\n",
      "15      74   71.06  2.94     3.97\n",
      "16      83   84.73  1.73     2.08\n",
      "17      75   71.06  3.94     5.25\n",
      "18      67   70.98  3.98     5.94\n",
      "19      80   84.98  4.98     6.22\n",
      "\n",
      "最佳模型(AdaBoost)性能统计:\n",
      "平均绝对误差: 4.12\n",
      "最大绝对误差: 9.03\n",
      "平均相对误差: 5.47%\n",
      "\n",
      "======================================================================\n",
      "模型解释性总结\n",
      "======================================================================\n",
      "\n",
      "1. 线性回归 - 最重要的5个特征:\n",
      "     特征名称        系数\n",
      "4  历史违约次数 -1.383324\n",
      "2      性别  0.393293\n",
      "1      年龄  0.168401\n",
      "0     月收入  0.000599\n",
      "3  历史授信额度  0.000066\n",
      "\n",
      "2. 随机森林 - 最重要的5个特征:\n",
      "     特征名称       重要性\n",
      "0     月收入  0.414820\n",
      "3  历史授信额度  0.270905\n",
      "4  历史违约次数  0.207646\n",
      "1      年龄  0.095011\n",
      "2      性别  0.011618\n",
      "\n",
      "3. 梯度提升树 - 最重要的5个特征:\n",
      "     特征名称       重要性\n",
      "0     月收入  0.582768\n",
      "3  历史授信额度  0.216526\n",
      "4  历史违约次数  0.162290\n",
      "1      年龄  0.035622\n",
      "2      性别  0.002793\n",
      "\n",
      "🎯 最终推荐: 使用 AdaBoost 模型进行信用评分预测\n",
      "📊 模型解释力(R²): 0.733\n",
      "🎯 预测精度(RMSE): 4.03 分\n"
     ]
    }
   ],
   "source": [
    "# 设置中文字体支持\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 1.读取数据\n",
    "import pandas as pd\n",
    "df = pd.read_excel(\"D:\\贺之恒的python\\pppython\\python商业数据分析\\@Python大数据分析与机器学习商业案例实战\\第10章 机器学习神器：XGBoost&LightGBM模型\\源代码汇总_Pycharm\\信用评分卡模型.xlsx\")\n",
    "print(\"数据形状:\", df.shape)\n",
    "print(\"\\n前5行数据:\")\n",
    "print(df.head())\n",
    "\n",
    "print(\"\\n数据描述性统计:\")\n",
    "print(df.describe())\n",
    "\n",
    "# 2.提取特征变量和目标变量\n",
    "X = df.drop(columns='信用评分')\n",
    "y = df['信用评分']\n",
    "\n",
    "print(f\"\\n特征变量形状: {X.shape}\")\n",
    "print(f\"目标变量统计: 均值={y.mean():.2f}, 标准差={y.std():.2f}, 范围=[{y.min():.2f}, {y.max():.2f}]\")\n",
    "\n",
    "# 3.划分训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n",
    "\n",
    "print(f\"\\n训练集形状: {X_train.shape}, 测试集形状: {X_test.shape}\")\n",
    "\n",
    "# 4.导入多种回归模型\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "import numpy as np\n",
    "\n",
    "# 创建模型字典\n",
    "models = {\n",
    "    '线性回归': LinearRegression(),\n",
    "    '岭回归': Ridge(random_state=123),\n",
    "    'Lasso回归': Lasso(random_state=123),\n",
    "    '弹性网络': ElasticNet(random_state=123),\n",
    "    '决策树': DecisionTreeRegressor(random_state=123),\n",
    "    '随机森林': RandomForestRegressor(random_state=123),\n",
    "    '梯度提升树': GradientBoostingRegressor(random_state=123),\n",
    "    'AdaBoost': AdaBoostRegressor(random_state=123),\n",
    "    'XGBoost': XGBRegressor(random_state=123),\n",
    "    'SVM回归': SVR(),\n",
    "    'K近邻': KNeighborsRegressor()\n",
    "}\n",
    "\n",
    "# 存储结果\n",
    "results = {}\n",
    "\n",
    "# 5.训练和评估各个模型\n",
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"各回归模型性能评估\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    print(f\"\\n=== {model_name} ===\")\n",
    "    \n",
    "    # 模型训练\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # 模型预测\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # 评估指标\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    \n",
    "    print(f\"R²分数: {r2:.4f}\")\n",
    "    print(f\"均方根误差(RMSE): {rmse:.4f}\")\n",
    "    print(f\"平均绝对误差(MAE): {mae:.4f}\")\n",
    "    \n",
    "    # 存储结果\n",
    "    results[model_name] = {\n",
    "        'model': model,\n",
    "        'r2': r2,\n",
    "        'rmse': rmse,\n",
    "        'mae': mae,\n",
    "        'y_pred': y_pred\n",
    "    }\n",
    "\n",
    "# 6.比较模型性能\n",
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"模型性能比较汇总\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "performance_df = pd.DataFrame({\n",
    "    '模型': list(results.keys()),\n",
    "    'R²分数': [result['r2'] for result in results.values()],\n",
    "    'RMSE': [result['rmse'] for result in results.values()],\n",
    "    'MAE': [result['mae'] for result in results.values()]\n",
    "}).sort_values('R²分数', ascending=False)\n",
    "\n",
    "print(performance_df)\n",
    "\n",
    "# 7.可视化模型性能比较\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "\n",
    "# R²分数比较\n",
    "bars1 = axes[0,0].bar(performance_df['模型'], performance_df['R²分数'], color='skyblue', alpha=0.8)\n",
    "axes[0,0].set_title('各模型R²分数比较', fontsize=14)\n",
    "axes[0,0].set_ylabel('R²分数', fontsize=12)\n",
    "axes[0,0].tick_params(axis='x', rotation=45)\n",
    "# 在柱状图上添加数值\n",
    "for bar in bars1:\n",
    "    height = bar.get_height()\n",
    "    axes[0,0].text(bar.get_x() + bar.get_width()/2., height,\n",
    "                  f'{height:.3f}', ha='center', va='bottom')\n",
    "\n",
    "# RMSE比较\n",
    "bars2 = axes[0,1].bar(performance_df['模型'], performance_df['RMSE'], color='lightcoral', alpha=0.8)\n",
    "axes[0,1].set_title('各模型RMSE比较', fontsize=14)\n",
    "axes[0,1].set_ylabel('RMSE', fontsize=12)\n",
    "axes[0,1].tick_params(axis='x', rotation=45)\n",
    "for bar in bars2:\n",
    "    height = bar.get_height()\n",
    "    axes[0,1].text(bar.get_x() + bar.get_width()/2., height,\n",
    "                  f'{height:.3f}', ha='center', va='bottom')\n",
    "\n",
    "# MAE比较\n",
    "bars3 = axes[1,0].bar(performance_df['模型'], performance_df['MAE'], color='lightgreen', alpha=0.8)\n",
    "axes[1,0].set_title('各模型MAE比较', fontsize=14)\n",
    "axes[1,0].set_ylabel('MAE', fontsize=12)\n",
    "axes[1,0].tick_params(axis='x', rotation=45)\n",
    "for bar in bars3:\n",
    "    height = bar.get_height()\n",
    "    axes[1,0].text(bar.get_x() + bar.get_width()/2., height,\n",
    "                  f'{height:.3f}', ha='center', va='bottom')\n",
    "\n",
    "# 预测值与实际值散点图（最佳模型）\n",
    "best_model_name = performance_df.iloc[0]['模型']\n",
    "best_y_pred = results[best_model_name]['y_pred']\n",
    "axes[1,1].scatter(y_test, best_y_pred, alpha=0.6, color='purple')\n",
    "axes[1,1].plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
    "axes[1,1].set_xlabel('实际信用评分', fontsize=12)\n",
    "axes[1,1].set_ylabel('预测信用评分', fontsize=12)\n",
    "axes[1,1].set_title(f'{best_model_name} - 预测值 vs 实际值\\nR² = {performance_df.iloc[0][\"R²分数\"]:.3f}', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 8.特征重要性分析\n",
    "print(\"\\n\" + \"=\"*70)\n",
    "print(\"特征重要性分析\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "# 线性回归系数分析\n",
    "lr_model = results['线性回归']['model']\n",
    "lr_coefficients = pd.DataFrame({\n",
    "    '特征名称': X.columns,\n",
    "    '系数': lr_model.coef_,\n",
    "    '系数绝对值': abs(lr_model.coef_)\n",
    "}).sort_values('系数绝对值', ascending=False)\n",
    "\n",
    "print(\"线性回归系数分析:\")\n",
    "print(lr_coefficients)\n",
    "\n",
    "# 可视化线性回归系数\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "top_coeff = lr_coefficients.head(10).sort_values('系数', ascending=True)\n",
    "plt.barh(top_coeff['特征名称'], top_coeff['系数'], color='steelblue')\n",
    "plt.xlabel('系数值')\n",
    "plt.title('线性回归 - 前10重要特征系数')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# 随机森林的特征重要性\n",
    "rf_model = results['随机森林']['model']\n",
    "rf_importances = pd.DataFrame({\n",
    "    '特征名称': X.columns,\n",
    "    '重要性': rf_model.feature_importances_\n",
    "}).sort_values('重要性', ascending=False)\n",
    "\n",
    "print(\"\\n随机森林特征重要性:\")\n",
    "print(rf_importances.head(10))\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.barh(rf_importances['特征名称'][:10], rf_importances['重要性'][:10], color='forestgreen')\n",
    "plt.xlabel('特征重要性')\n",
    "plt.title('随机森林 - 前10重要特征')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 9.梯度提升树和XGBoost特征重要性对比\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "# 梯度提升树特征重要性\n",
    "gbdt_model = results['梯度提升树']['model']\n",
    "gbdt_importances = pd.DataFrame({\n",
    "    '特征名称': X.columns,\n",
    "    '重要性': gbdt_model.feature_importances_\n",
    "}).sort_values('重要性', ascending=False)\n",
    "\n",
    "print(\"\\n梯度提升树特征重要性:\")\n",
    "print(gbdt_importances.head(10))\n",
    "\n",
    "axes[0].barh(gbdt_importances['特征名称'][:10], gbdt_importances['重要性'][:10], color='coral')\n",
    "axes[0].set_xlabel('特征重要性')\n",
    "axes[0].set_title('梯度提升树 - 前10重要特征')\n",
    "axes[0].invert_yaxis()\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "# XGBoost特征重要性\n",
    "xgb_model = results['XGBoost']['model']\n",
    "xgb_importances = pd.DataFrame({\n",
    "    '特征名称': X.columns,\n",
    "    '重要性': xgb_model.feature_importances_\n",
    "}).sort_values('重要性', ascending=False)\n",
    "\n",
    "print(\"\\nXGBoost特征重要性:\")\n",
    "print(xgb_importances.head(10))\n",
    "\n",
    "axes[1].barh(xgb_importances['特征名称'][:10], xgb_importances['重要性'][:10], color='gold')\n",
    "axes[1].set_xlabel('特征重要性')\n",
    "axes[1].set_title('XGBoost - 前10重要特征')\n",
    "axes[1].invert_yaxis()\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 10.选择最佳模型并进行详细分析\n",
    "best_model_name = performance_df.iloc[0]['模型']\n",
    "best_model = results[best_model_name]['model']\n",
    "best_r2 = performance_df.iloc[0]['R²分数']\n",
    "\n",
    "print(f\"\\n\" + \"=\"*70)\n",
    "print(f\"最佳模型: {best_model_name}\")\n",
    "print(f\"最佳R²分数: {best_r2:.4f}\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "# 使用最佳模型进行详细预测分析\n",
    "best_y_pred = results[best_model_name]['y_pred']\n",
    "\n",
    "# 11.残差分析\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "# 残差图\n",
    "plt.subplot(1, 3, 1)\n",
    "residuals = y_test - best_y_pred\n",
    "plt.scatter(best_y_pred, residuals, alpha=0.6, color='blue')\n",
    "plt.axhline(y=0, color='red', linestyle='--', linewidth=2)\n",
    "plt.xlabel('预测值')\n",
    "plt.ylabel('残差')\n",
    "plt.title(f'{best_model_name} - 残差图')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# 预测分布\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.hist(y_test, alpha=0.7, label='实际值', bins=20, color='blue', edgecolor='black')\n",
    "plt.hist(best_y_pred, alpha=0.7, label='预测值', bins=20, color='red', edgecolor='black')\n",
    "plt.xlabel('信用评分')\n",
    "plt.ylabel('频数')\n",
    "plt.title('实际值 vs 预测值分布')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# 预测误差分布\n",
    "plt.subplot(1, 3, 3)\n",
    "errors = abs(y_test - best_y_pred)\n",
    "plt.hist(errors, bins=20, color='green', alpha=0.7, edgecolor='black')\n",
    "plt.xlabel('绝对误差')\n",
    "plt.ylabel('频数')\n",
    "plt.title('预测误差分布')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 12.详细预测结果对比\n",
    "print(\"\\n前20个样本的详细预测结果:\")\n",
    "comparison_df = pd.DataFrame({\n",
    "    '实际信用评分': y_test.values[:20],\n",
    "    '预测信用评分': best_y_pred[:20],\n",
    "    '绝对误差': abs(y_test.values[:20] - best_y_pred[:20]),\n",
    "    '相对误差(%)': (abs(y_test.values[:20] - best_y_pred[:20]) / y_test.values[:20] * 100)\n",
    "})\n",
    "\n",
    "print(comparison_df.round(2))\n",
    "\n",
    "# 13.模型性能统计\n",
    "print(f\"\\n最佳模型({best_model_name})性能统计:\")\n",
    "print(f\"平均绝对误差: {comparison_df['绝对误差'].mean():.2f}\")\n",
    "print(f\"最大绝对误差: {comparison_df['绝对误差'].max():.2f}\")\n",
    "print(f\"平均相对误差: {comparison_df['相对误差(%)'].mean():.2f}%\")\n",
    "\n",
    "# 14.模型系数/重要性总结\n",
    "print(f\"\\n{'='*70}\")\n",
    "print(\"模型解释性总结\")\n",
    "print(f\"{'='*70}\")\n",
    "\n",
    "print(\"\\n1. 线性回归 - 最重要的5个特征:\")\n",
    "print(lr_coefficients.head(5)[['特征名称', '系数']])\n",
    "\n",
    "print(\"\\n2. 随机森林 - 最重要的5个特征:\")\n",
    "print(rf_importances.head(5)[['特征名称', '重要性']])\n",
    "\n",
    "print(\"\\n3. 梯度提升树 - 最重要的5个特征:\")\n",
    "print(gbdt_importances.head(5)[['特征名称', '重要性']])\n",
    "\n",
    "print(f\"\\n🎯 最终推荐: 使用 {best_model_name} 模型进行信用评分预测\")\n",
    "print(f\"📊 模型解释力(R²): {best_r2:.3f}\")\n",
    "print(f\"🎯 预测精度(RMSE): {performance_df.iloc[0]['RMSE']:.2f} 分\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99dbc275",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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